Overview

Dataset statistics

Number of variables45
Number of observations173
Missing cells198
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.9 KiB
Average record size in memory360.7 B

Variable types

Numeric26
Categorical18
Boolean1

Alerts

name has a high cardinality: 143 distinct valuesHigh cardinality
rear tyre size has a high cardinality: 61 distinct valuesHigh cardinality
chassis type has a high cardinality: 90 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with price and 13 other fieldsHigh correlation
price is highly overall correlated with Unnamed: 0 and 23 other fieldsHigh correlation
max power is highly overall correlated with Unnamed: 0 and 23 other fieldsHigh correlation
max torque is highly overall correlated with Unnamed: 0 and 23 other fieldsHigh correlation
displacement is highly overall correlated with Unnamed: 0 and 25 other fieldsHigh correlation
bore is highly overall correlated with Unnamed: 0 and 22 other fieldsHigh correlation
stroke is highly overall correlated with transmission and 5 other fieldsHigh correlation
fuel tank capacity is highly overall correlated with price and 27 other fieldsHigh correlation
mileage - arai is highly overall correlated with price and 23 other fieldsHigh correlation
mileage - owner reported is highly overall correlated with price and 18 other fieldsHigh correlation
top speed is highly overall correlated with Unnamed: 0 and 27 other fieldsHigh correlation
front brake size is highly overall correlated with Unnamed: 0 and 26 other fieldsHigh correlation
rear brake size is highly overall correlated with Unnamed: 0 and 27 other fieldsHigh correlation
front wheel size is highly overall correlated with fuel tank capacity and 10 other fieldsHigh correlation
rear wheel size is highly overall correlated with front wheel size and 9 other fieldsHigh correlation
front tyre pressure (rider) is highly overall correlated with price and 25 other fieldsHigh correlation
rear tyre pressure (rider) is highly overall correlated with price and 16 other fieldsHigh correlation
front tyre pressure (rider & pillion) is highly overall correlated with price and 23 other fieldsHigh correlation
rear tyre pressure (rider & pillion) is highly overall correlated with front tyre pressure (rider) and 3 other fieldsHigh correlation
kerb weight is highly overall correlated with price and 26 other fieldsHigh correlation
overall length is highly overall correlated with price and 26 other fieldsHigh correlation
overall width is highly overall correlated with price and 18 other fieldsHigh correlation
wheelbase is highly overall correlated with Unnamed: 0 and 24 other fieldsHigh correlation
ground clearance is highly overall correlated with rear tyre sizeHigh correlation
seat height is highly overall correlated with max power and 6 other fieldsHigh correlation
overall height is highly overall correlated with chassis typeHigh correlation
brand is highly overall correlated with Unnamed: 0 and 10 other fieldsHigh correlation
cooling system is highly overall correlated with max torque and 9 other fieldsHigh correlation
transmission is highly overall correlated with price and 25 other fieldsHigh correlation
transmission type is highly overall correlated with price and 23 other fieldsHigh correlation
cylinders is highly overall correlated with price and 20 other fieldsHigh correlation
valves per cylinder is highly overall correlated with fuel tank capacity and 8 other fieldsHigh correlation
spark plugs is highly overall correlated with mileage - arai and 2 other fieldsHigh correlation
gear shifting pattern is highly overall correlated with mileage - arai and 10 other fieldsHigh correlation
clutch is highly overall correlated with Unnamed: 0 and 15 other fieldsHigh correlation
braking system is highly overall correlated with mileage - arai and 7 other fieldsHigh correlation
front brake type is highly overall correlated with bore and 24 other fieldsHigh correlation
rear tyre size is highly overall correlated with Unnamed: 0 and 36 other fieldsHigh correlation
tyre type is highly overall correlated with mileage - arai and 4 other fieldsHigh correlation
radial tyres is highly overall correlated with Unnamed: 0 and 27 other fieldsHigh correlation
rear brake type is highly overall correlated with max power and 32 other fieldsHigh correlation
wheel type is highly overall correlated with front wheel size and 3 other fieldsHigh correlation
front tyre size is highly overall correlated with price and 29 other fieldsHigh correlation
chassis type is highly overall correlated with Unnamed: 0 and 24 other fieldsHigh correlation
transmission type is highly imbalanced (50.9%)Imbalance
spark plugs is highly imbalanced (69.2%)Imbalance
tyre type is highly imbalanced (51.8%)Imbalance
mileage - arai has 135 (78.0%) missing valuesMissing
mileage - owner reported has 63 (36.4%) missing valuesMissing
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2023-04-07 11:59:45.410980
Analysis finished2023-04-07 12:00:58.278798
Duration1 minute and 12.87 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.884393
Minimum0
Maximum192
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:30:58.379161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.6
Q143
median86
Q3132
95-th percentile183.4
Maximum192
Range192
Interquartile range (IQR)89

Descriptive statistics

Standard deviation56.699108
Coefficient of variation (CV)0.62385968
Kurtosis-1.1334224
Mean90.884393
Median Absolute Deviation (MAD)45
Skewness0.20998177
Sum15723
Variance3214.7889
MonotonicityStrictly increasing
2023-04-07T17:30:58.496786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.6%
120 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (163) 163
94.2%
ValueCountFrequency (%)
0 1
0.6%
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
ValueCountFrequency (%)
192 1
0.6%
191 1
0.6%
190 1
0.6%
189 1
0.6%
188 1
0.6%
187 1
0.6%
186 1
0.6%
185 1
0.6%
184 1
0.6%
183 1
0.6%

name
Categorical

Distinct143
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Pulsar
 
9
Ninja
 
5
Apache
 
5
Scrambler
 
4
Duke
 
4
Other values (138)
146 

Length

Max length32
Median length22
Mean length14.184971
Min length2

Characters and Unicode

Total characters2454
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)75.7%

Sample

1st rowRoyal Enfield Hunter 350
2nd rowRoyal Enfield Classic 350
3rd rowRoyal Enfield Bullet 350
4th rowRoyal Enfield Continental GT 650
5th rowRoyal Enfield Meteor 350

Common Values

ValueCountFrequency (%)
Pulsar 9
 
5.2%
Ninja 5
 
2.9%
Apache 5
 
2.9%
Scrambler 4
 
2.3%
Duke 4
 
2.3%
Bonneville 3
 
1.7%
Platina 2
 
1.2%
Avenger 2
 
1.2%
Activa 2
 
1.2%
SR 2
 
1.2%
Other values (133) 135
78.0%

Length

2023-04-07T17:30:58.620225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
honda 17
 
3.9%
hero 15
 
3.5%
125 15
 
3.5%
ducati 12
 
2.8%
tvs 12
 
2.8%
yamaha 10
 
2.3%
pulsar 9
 
2.1%
triumph 9
 
2.1%
kawasaki 9
 
2.1%
royal 8
 
1.8%
Other values (182) 317
73.2%

Most occurring characters

ValueCountFrequency (%)
260
 
10.6%
a 203
 
8.3%
e 160
 
6.5%
i 122
 
5.0%
r 120
 
4.9%
o 97
 
4.0%
n 94
 
3.8%
l 91
 
3.7%
0 84
 
3.4%
t 79
 
3.2%
Other values (53) 1144
46.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1442
58.8%
Uppercase Letter 477
 
19.4%
Decimal Number 270
 
11.0%
Space Separator 260
 
10.6%
Math Symbol 2
 
0.1%
Dash Punctuation 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 203
14.1%
e 160
11.1%
i 122
 
8.5%
r 120
 
8.3%
o 97
 
6.7%
n 94
 
6.5%
l 91
 
6.3%
t 79
 
5.5%
u 75
 
5.2%
s 56
 
3.9%
Other values (16) 345
23.9%
Uppercase Letter
ValueCountFrequency (%)
S 67
14.0%
T 43
 
9.0%
H 42
 
8.8%
V 36
 
7.5%
R 32
 
6.7%
D 27
 
5.7%
B 26
 
5.5%
X 25
 
5.2%
P 23
 
4.8%
C 20
 
4.2%
Other values (14) 136
28.5%
Decimal Number
ValueCountFrequency (%)
0 84
31.1%
5 55
20.4%
1 44
16.3%
2 40
14.8%
6 17
 
6.3%
3 11
 
4.1%
4 11
 
4.1%
9 6
 
2.2%
8 2
 
0.7%
Space Separator
ValueCountFrequency (%)
260
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1919
78.2%
Common 535
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 203
 
10.6%
e 160
 
8.3%
i 122
 
6.4%
r 120
 
6.3%
o 97
 
5.1%
n 94
 
4.9%
l 91
 
4.7%
t 79
 
4.1%
u 75
 
3.9%
S 67
 
3.5%
Other values (40) 811
42.3%
Common
ValueCountFrequency (%)
260
48.6%
0 84
 
15.7%
5 55
 
10.3%
1 44
 
8.2%
2 40
 
7.5%
6 17
 
3.2%
3 11
 
2.1%
4 11
 
2.1%
9 6
 
1.1%
+ 2
 
0.4%
Other values (3) 5
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
 
10.6%
a 203
 
8.3%
e 160
 
6.5%
i 122
 
5.0%
r 120
 
4.9%
o 97
 
4.0%
n 94
 
3.8%
l 91
 
3.7%
0 84
 
3.4%
t 79
 
3.2%
Other values (53) 1144
46.6%

brand
Categorical

Distinct17
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Honda
21 
Hero
17 
Bajaj
17 
TVS
17 
Ducati
16 
Other values (12)
85 

Length

Max length13
Median length8
Mean length5.7919075
Min length3

Characters and Unicode

Total characters1002
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Enfield
2nd rowRoyal Enfield
3rd rowRoyal Enfield
4th rowRoyal Enfield
5th rowRoyal Enfield

Common Values

ValueCountFrequency (%)
Honda 21
12.1%
Hero 17
9.8%
Bajaj 17
9.8%
TVS 17
9.8%
Ducati 16
9.2%
Kawasaki 14
8.1%
Triumph 12
 
6.9%
Yamaha 10
 
5.8%
Royal Enfield 8
 
4.6%
Suzuki 8
 
4.6%
Other values (7) 33
19.1%

Length

2023-04-07T17:30:58.728260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
honda 21
11.6%
hero 17
9.4%
bajaj 17
9.4%
tvs 17
9.4%
ducati 16
 
8.8%
kawasaki 14
 
7.7%
triumph 12
 
6.6%
yamaha 10
 
5.5%
suzuki 8
 
4.4%
enfield 8
 
4.4%
Other values (8) 41
22.7%

Most occurring characters

ValueCountFrequency (%)
a 172
17.2%
i 78
 
7.8%
o 54
 
5.4%
u 46
 
4.6%
e 43
 
4.3%
H 40
 
4.0%
r 38
 
3.8%
n 37
 
3.7%
l 35
 
3.5%
T 35
 
3.5%
Other values (29) 424
42.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 759
75.7%
Uppercase Letter 235
 
23.5%
Space Separator 8
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 172
22.7%
i 78
10.3%
o 54
 
7.1%
u 46
 
6.1%
e 43
 
5.7%
r 38
 
5.0%
n 37
 
4.9%
l 35
 
4.6%
j 34
 
4.5%
d 29
 
3.8%
Other values (13) 193
25.4%
Uppercase Letter
ValueCountFrequency (%)
H 40
17.0%
T 35
14.9%
S 25
10.6%
V 23
9.8%
B 23
9.8%
K 20
8.5%
D 16
 
6.8%
Y 10
 
4.3%
M 10
 
4.3%
E 8
 
3.4%
Other values (5) 25
10.6%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 994
99.2%
Common 8
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 172
17.3%
i 78
 
7.8%
o 54
 
5.4%
u 46
 
4.6%
e 43
 
4.3%
H 40
 
4.0%
r 38
 
3.8%
n 37
 
3.7%
l 35
 
3.5%
T 35
 
3.5%
Other values (28) 416
41.9%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 172
17.2%
i 78
 
7.8%
o 54
 
5.4%
u 46
 
4.6%
e 43
 
4.3%
H 40
 
4.0%
r 38
 
3.8%
n 37
 
3.7%
l 35
 
3.5%
T 35
 
3.5%
Other values (29) 424
42.3%

price
Real number (ℝ)

Distinct172
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567831.99
Minimum45648
Maximum7990000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:30:58.831393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum45648
5-th percentile70078.2
Q1105597
median174573
Q3753951
95-th percentile2131200
Maximum7990000
Range7944352
Interquartile range (IQR)648354

Descriptive statistics

Standard deviation888028.15
Coefficient of variation (CV)1.5638924
Kurtosis28.717684
Mean567831.99
Median Absolute Deviation (MAD)97195
Skewness4.2498693
Sum98234935
Variance7.88594 × 1011
MonotonicityNot monotonic
2023-04-07T17:30:58.957414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1285000 2
 
1.2%
149900 1
 
0.6%
340000 1
 
0.6%
902000 1
 
0.6%
513959 1
 
0.6%
712000 1
 
0.6%
643034 1
 
0.6%
1209967 1
 
0.6%
640000 1
 
0.6%
2301733 1
 
0.6%
Other values (162) 162
93.6%
ValueCountFrequency (%)
45648 1
0.6%
47140 1
0.6%
52734 1
0.6%
59041 1
0.6%
60163 1
0.6%
61577 1
0.6%
64580 1
0.6%
67659 1
0.6%
68847 1
0.6%
70899 1
0.6%
ValueCountFrequency (%)
7990000 1
0.6%
3988635 1
0.6%
2741000 1
0.6%
2372222 1
0.6%
2369000 1
0.6%
2301733 1
0.6%
2215000 1
0.6%
2149000 1
0.6%
2148000 1
0.6%
2120000 1
0.6%

max power
Real number (ℝ)

Distinct125
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.971214
Minimum4.3
Maximum305.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:30:59.085265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile7.906
Q110.7
median20.7
Q365.7
95-th percentile169.604
Maximum305.75
Range301.45
Interquartile range (IQR)55

Descriptive statistics

Standard deviation55.216506
Coefficient of variation (CV)1.1755393
Kurtosis3.7724616
Mean46.971214
Median Absolute Deviation (MAD)12.26
Skewness1.9540102
Sum8126.02
Variance3048.8625
MonotonicityNot monotonic
2023-04-07T17:30:59.204121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.78 5
 
2.9%
8.04 4
 
2.3%
71.87 4
 
2.3%
10.72 4
 
2.3%
20.2 3
 
1.7%
64.1 3
 
1.7%
55.65 3
 
1.7%
46.8 3
 
1.7%
12.2 3
 
1.7%
24.1 3
 
1.7%
Other values (115) 138
79.8%
ValueCountFrequency (%)
4.3 2
1.2%
5.36 1
 
0.6%
7.65 1
 
0.6%
7.68 1
 
0.6%
7.71 1
 
0.6%
7.77 1
 
0.6%
7.9 2
1.2%
7.91 2
1.2%
8 2
1.2%
8.04 4
2.3%
ValueCountFrequency (%)
305.75 1
0.6%
215 1
0.6%
213.89 1
0.6%
212.5 1
0.6%
205.17 1
0.6%
200.21 1
0.6%
197.26 1
0.6%
177.5 1
0.6%
172.58 1
0.6%
167.62 1
0.6%

max torque
Real number (ℝ)

Distinct105
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.109364
Minimum6.5
Maximum221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:30:59.452021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile8.7
Q110.6
median21.5
Q362.9
95-th percentile124.16
Maximum221
Range214.5
Interquartile range (IQR)52.3

Descriptive statistics

Standard deviation41.438487
Coefficient of variation (CV)1.008006
Kurtosis1.8648385
Mean41.109364
Median Absolute Deviation (MAD)11.8
Skewness1.4926995
Sum7111.92
Variance1717.1482
MonotonicityNot monotonic
2023-04-07T17:30:59.567861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.7 6
 
3.5%
10.6 6
 
3.5%
10.3 4
 
2.3%
66.2 4
 
2.3%
46 4
 
2.3%
24 4
 
2.3%
9.6 4
 
2.3%
64 4
 
2.3%
27 3
 
1.7%
6.5 3
 
1.7%
Other values (95) 131
75.7%
ValueCountFrequency (%)
6.5 3
1.7%
8.05 3
1.7%
8.34 1
 
0.6%
8.7 6
3.5%
8.8 2
 
1.2%
8.84 1
 
0.6%
9 1
 
0.6%
9.2 1
 
0.6%
9.3 2
 
1.2%
9.6 4
2.3%
ValueCountFrequency (%)
221 1
 
0.6%
170 1
 
0.6%
165 1
 
0.6%
137 1
 
0.6%
129 1
 
0.6%
127.4 1
 
0.6%
125 3
1.7%
123.6 1
 
0.6%
123 1
 
0.6%
122 1
 
0.6%

cooling system
Categorical

Distinct6
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
air cooled
76 
liquid cooled
73 
oil cooled
15 
air/oil cooled
 
6
fan cooled
 
2

Length

Max length14
Median length10
Mean length11.416185
Min length10

Characters and Unicode

Total characters1975
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowair/oil cooled
2nd rowair/oil cooled
3rd rowair cooled
4th rowair/oil cooled
5th rowair/oil cooled

Common Values

ValueCountFrequency (%)
air cooled 76
43.9%
liquid cooled 73
42.2%
oil cooled 15
 
8.7%
air/oil cooled 6
 
3.5%
fan cooled 2
 
1.2%
water cooled 1
 
0.6%

Length

2023-04-07T17:30:59.678196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:30:59.825068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cooled 173
50.0%
air 76
22.0%
liquid 73
21.1%
oil 15
 
4.3%
air/oil 6
 
1.7%
fan 2
 
0.6%
water 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 367
18.6%
l 267
13.5%
i 249
12.6%
d 246
12.5%
e 174
8.8%
173
8.8%
c 173
8.8%
a 85
 
4.3%
r 83
 
4.2%
q 73
 
3.7%
Other values (6) 85
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1796
90.9%
Space Separator 173
 
8.8%
Other Punctuation 6
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 367
20.4%
l 267
14.9%
i 249
13.9%
d 246
13.7%
e 174
9.7%
c 173
9.6%
a 85
 
4.7%
r 83
 
4.6%
q 73
 
4.1%
u 73
 
4.1%
Other values (4) 6
 
0.3%
Space Separator
ValueCountFrequency (%)
173
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1796
90.9%
Common 179
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 367
20.4%
l 267
14.9%
i 249
13.9%
d 246
13.7%
e 174
9.7%
c 173
9.6%
a 85
 
4.7%
r 83
 
4.6%
q 73
 
4.1%
u 73
 
4.1%
Other values (4) 6
 
0.3%
Common
ValueCountFrequency (%)
173
96.6%
/ 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 367
18.6%
l 267
13.5%
i 249
12.6%
d 246
12.5%
e 174
8.8%
173
8.8%
c 173
8.8%
a 85
 
4.3%
r 83
 
4.2%
q 73
 
3.7%
Other values (6) 85
 
4.3%

transmission
Categorical

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
6 speed manual
79 
5 speed manual
49 
automatic
32 
4 speed manual
12 
7 speed automatic
 
1

Length

Max length17
Median length14
Mean length13.092486
Min length9

Characters and Unicode

Total characters2265
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row5 speed manual
2nd row5 speed manual
3rd row5 speed manual
4th row6 speed manual
5th row5 speed manual

Common Values

ValueCountFrequency (%)
6 speed manual 79
45.7%
5 speed manual 49
28.3%
automatic 32
18.5%
4 speed manual 12
 
6.9%
7 speed automatic 1
 
0.6%

Length

2023-04-07T17:30:59.948983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:00.098118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
speed 141
31.0%
manual 140
30.8%
6 79
17.4%
5 49
 
10.8%
automatic 33
 
7.3%
4 12
 
2.6%
7 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 346
15.3%
e 282
12.5%
282
12.5%
u 173
7.6%
m 173
7.6%
s 141
6.2%
p 141
6.2%
d 141
6.2%
l 140
6.2%
n 140
6.2%
Other values (8) 306
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1842
81.3%
Space Separator 282
 
12.5%
Decimal Number 141
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 346
18.8%
e 282
15.3%
u 173
9.4%
m 173
9.4%
s 141
7.7%
p 141
7.7%
d 141
7.7%
l 140
7.6%
n 140
7.6%
t 66
 
3.6%
Other values (3) 99
 
5.4%
Decimal Number
ValueCountFrequency (%)
6 79
56.0%
5 49
34.8%
4 12
 
8.5%
7 1
 
0.7%
Space Separator
ValueCountFrequency (%)
282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1842
81.3%
Common 423
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 346
18.8%
e 282
15.3%
u 173
9.4%
m 173
9.4%
s 141
7.7%
p 141
7.7%
d 141
7.7%
l 140
7.6%
n 140
7.6%
t 66
 
3.6%
Other values (3) 99
 
5.4%
Common
ValueCountFrequency (%)
282
66.7%
6 79
 
18.7%
5 49
 
11.6%
4 12
 
2.8%
7 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 346
15.3%
e 282
12.5%
282
12.5%
u 173
7.6%
m 173
7.6%
s 141
6.2%
p 141
6.2%
d 141
6.2%
l 140
6.2%
n 140
6.2%
Other values (8) 306
13.5%

transmission type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
chain drive
139 
cvt
32 
shaft drive
 
2

Length

Max length11
Median length11
Mean length9.5202312
Min length3

Characters and Unicode

Total characters1647
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowchain drive
2nd rowchain drive
3rd rowchain drive
4th rowchain drive
5th rowchain drive

Common Values

ValueCountFrequency (%)
chain drive 139
80.3%
cvt 32
 
18.5%
shaft drive 2
 
1.2%

Length

2023-04-07T17:31:00.221858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:00.346907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
drive 141
44.9%
chain 139
44.3%
cvt 32
 
10.2%
shaft 2
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i 280
17.0%
v 173
10.5%
c 171
10.4%
h 141
8.6%
a 141
8.6%
141
8.6%
d 141
8.6%
r 141
8.6%
e 141
8.6%
n 139
8.4%
Other values (3) 38
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1506
91.4%
Space Separator 141
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 280
18.6%
v 173
11.5%
c 171
11.4%
h 141
9.4%
a 141
9.4%
d 141
9.4%
r 141
9.4%
e 141
9.4%
n 139
9.2%
t 34
 
2.3%
Other values (2) 4
 
0.3%
Space Separator
ValueCountFrequency (%)
141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1506
91.4%
Common 141
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 280
18.6%
v 173
11.5%
c 171
11.4%
h 141
9.4%
a 141
9.4%
d 141
9.4%
r 141
9.4%
e 141
9.4%
n 139
9.2%
t 34
 
2.3%
Other values (2) 4
 
0.3%
Common
ValueCountFrequency (%)
141
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 280
17.0%
v 173
10.5%
c 171
10.4%
h 141
8.6%
a 141
8.6%
141
8.6%
d 141
8.6%
r 141
8.6%
e 141
8.6%
n 139
8.4%
Other values (3) 38
 
2.3%

displacement
Real number (ℝ)

Distinct84
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.43942
Minimum87.8
Maximum2458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:00.475279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum87.8
5-th percentile109.51
Q1124.8
median248.76
Q3649
95-th percentile1158.8
Maximum2458
Range2370.2
Interquartile range (IQR)524.2

Descriptive statistics

Standard deviation402.9611
Coefficient of variation (CV)0.92968264
Kurtosis3.096572
Mean433.43942
Median Absolute Deviation (MAD)124.76
Skewness1.5549345
Sum74985.02
Variance162377.65
MonotonicityNot monotonic
2023-04-07T17:31:00.619795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124.45 7
 
4.0%
649 6
 
3.5%
249 6
 
3.5%
155 6
 
3.5%
109.7 5
 
2.9%
124 5
 
2.9%
149.5 4
 
2.3%
124.7 4
 
2.3%
1200 4
 
2.3%
803 4
 
2.3%
Other values (74) 122
70.5%
ValueCountFrequency (%)
87.8 1
 
0.6%
97.2 3
1.7%
99.7 2
 
1.2%
102 1
 
0.6%
109.51 4
2.3%
109.7 5
2.9%
110 2
 
1.2%
110.9 3
1.7%
115.45 2
 
1.2%
123.94 1
 
0.6%
ValueCountFrequency (%)
2458 1
 
0.6%
1833 1
 
0.6%
1262 2
1.2%
1200 4
2.3%
1160 1
 
0.6%
1158 1
 
0.6%
1103 2
1.2%
1099 1
 
0.6%
1082 1
 
0.6%
1079 1
 
0.6%

cylinders
Categorical

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1
113 
2
39 
4
15 
3
 
5
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters173
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 113
65.3%
2 39
 
22.5%
4 15
 
8.7%
3 5
 
2.9%
6 1
 
0.6%

Length

2023-04-07T17:31:00.737761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:00.871979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 113
65.3%
2 39
 
22.5%
4 15
 
8.7%
3 5
 
2.9%
6 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 113
65.3%
2 39
 
22.5%
4 15
 
8.7%
3 5
 
2.9%
6 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 173
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 113
65.3%
2 39
 
22.5%
4 15
 
8.7%
3 5
 
2.9%
6 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 113
65.3%
2 39
 
22.5%
4 15
 
8.7%
3 5
 
2.9%
6 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 113
65.3%
2 39
 
22.5%
4 15
 
8.7%
3 5
 
2.9%
6 1
 
0.6%

bore
Real number (ℝ)

Distinct40
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.50289
Minimum47
Maximum110.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:00.988987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile50
Q153.5
median69
Q378
95-th percentile97.6
Maximum110.2
Range63.2
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation15.257581
Coefficient of variation (CV)0.22272901
Kurtosis-0.6416337
Mean68.50289
Median Absolute Deviation (MAD)12
Skewness0.47693329
Sum11851
Variance232.79377
MonotonicityNot monotonic
2023-04-07T17:31:01.103001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
72 14
 
8.1%
50 14
 
8.1%
58 12
 
6.9%
83 9
 
5.2%
52 8
 
4.6%
76 8
 
4.6%
53.5 8
 
4.6%
52.4 7
 
4.0%
78 7
 
4.0%
81 6
 
3.5%
Other values (30) 80
46.2%
ValueCountFrequency (%)
47 5
 
2.9%
50 14
8.1%
51 3
 
1.7%
52 8
4.6%
52.4 7
4.0%
52.5 2
 
1.2%
53.5 8
4.6%
56 4
 
2.3%
57.3 6
3.5%
58 12
6.9%
ValueCountFrequency (%)
110.2 1
 
0.6%
106 2
1.2%
100 2
1.2%
98 1
 
0.6%
97.6 4
2.3%
94 4
2.3%
92 1
 
0.6%
90 1
 
0.6%
89 3
1.7%
88 4
2.3%

stroke
Real number (ℝ)

Distinct65
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.543815
Minimum43
Maximum90.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:01.238798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile48.8
Q156.6
median60
Q363.1
95-th percentile85.8
Maximum90.51
Range47.51
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation9.7214013
Coefficient of variation (CV)0.15795903
Kurtosis1.5397815
Mean61.543815
Median Absolute Deviation (MAD)3.3
Skewness1.2204989
Sum10647.08
Variance94.505644
MonotonicityNot monotonic
2023-04-07T17:31:01.369702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 11
 
6.4%
57.8 8
 
4.6%
58.6 8
 
4.6%
63.1 7
 
4.0%
80 7
 
4.0%
48.8 7
 
4.0%
66 5
 
2.9%
61.1 5
 
2.9%
57.9 5
 
2.9%
49 4
 
2.3%
Other values (55) 106
61.3%
ValueCountFrequency (%)
43 1
 
0.6%
46 2
 
1.2%
47.2 1
 
0.6%
48.5 1
 
0.6%
48.8 7
4.0%
49 4
2.3%
49.5 3
1.7%
50.5 1
 
0.6%
51.1 1
 
0.6%
51.8 1
 
0.6%
ValueCountFrequency (%)
90.51 1
 
0.6%
90.5 1
 
0.6%
90 2
 
1.2%
86 2
 
1.2%
85.9 1
 
0.6%
85.8 3
1.7%
83 1
 
0.6%
81.45 1
 
0.6%
80 7
4.0%
73 1
 
0.6%
Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
4
84 
2
69 
3
14 
1
 
5
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters173
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
4 84
48.6%
2 69
39.9%
3 14
 
8.1%
1 5
 
2.9%
8 1
 
0.6%

Length

2023-04-07T17:31:01.466416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:01.568431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 84
48.6%
2 69
39.9%
3 14
 
8.1%
1 5
 
2.9%
8 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
4 84
48.6%
2 69
39.9%
3 14
 
8.1%
1 5
 
2.9%
8 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 173
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 84
48.6%
2 69
39.9%
3 14
 
8.1%
1 5
 
2.9%
8 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 84
48.6%
2 69
39.9%
3 14
 
8.1%
1 5
 
2.9%
8 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 84
48.6%
2 69
39.9%
3 14
 
8.1%
1 5
 
2.9%
8 1
 
0.6%

spark plugs
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1 per cylinder
158 
2 per cylinder
 
12
3 per cylinder
 
3

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters2422
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 per cylinder
2nd row1 per cylinder
3rd row2 per cylinder
4th row1 per cylinder
5th row1 per cylinder

Common Values

ValueCountFrequency (%)
1 per cylinder 158
91.3%
2 per cylinder 12
 
6.9%
3 per cylinder 3
 
1.7%

Length

2023-04-07T17:31:01.666765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:01.775286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
per 173
33.3%
cylinder 173
33.3%
1 158
30.4%
2 12
 
2.3%
3 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
346
14.3%
e 346
14.3%
r 346
14.3%
p 173
7.1%
c 173
7.1%
y 173
7.1%
l 173
7.1%
i 173
7.1%
n 173
7.1%
d 173
7.1%
Other values (3) 173
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1903
78.6%
Space Separator 346
 
14.3%
Decimal Number 173
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 346
18.2%
r 346
18.2%
p 173
9.1%
c 173
9.1%
y 173
9.1%
l 173
9.1%
i 173
9.1%
n 173
9.1%
d 173
9.1%
Decimal Number
ValueCountFrequency (%)
1 158
91.3%
2 12
 
6.9%
3 3
 
1.7%
Space Separator
ValueCountFrequency (%)
346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1903
78.6%
Common 519
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 346
18.2%
r 346
18.2%
p 173
9.1%
c 173
9.1%
y 173
9.1%
l 173
9.1%
i 173
9.1%
n 173
9.1%
d 173
9.1%
Common
ValueCountFrequency (%)
346
66.7%
1 158
30.4%
2 12
 
2.3%
3 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
346
14.3%
e 346
14.3%
r 346
14.3%
p 173
7.1%
c 173
7.1%
y 173
7.1%
l 173
7.1%
i 173
7.1%
n 173
7.1%
d 173
7.1%
Other values (3) 173
7.1%
Distinct8
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1 down 5 up
79 
1 down 4 up
46 
automatic
32 
all 4 up
10 
all 4 down
 
2
Other values (3)
 
4

Length

Max length20
Median length11
Mean length10.468208
Min length8

Characters and Unicode

Total characters1811
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row1 down 4 up
2nd row1 down 4 up
3rd row1 down 4 up
4th row1 down 5 up
5th row1 down 4 up

Common Values

ValueCountFrequency (%)
1 down 5 up 79
45.7%
1 down 4 up 46
26.6%
automatic 32
18.5%
all 4 up 10
 
5.8%
all 4 down 2
 
1.2%
all 5 down 2
 
1.2%
all 5 up 1
 
0.6%
automatic/electronic 1
 
0.6%

Length

2023-04-07T17:31:01.877765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:02.013046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
up 136
23.5%
down 129
22.3%
1 125
21.6%
5 82
14.2%
4 58
10.0%
automatic 32
 
5.5%
all 15
 
2.6%
automatic/electronic 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
405
22.4%
u 169
9.3%
o 163
9.0%
p 136
 
7.5%
n 130
 
7.2%
d 129
 
7.1%
w 129
 
7.1%
1 125
 
6.9%
5 82
 
4.5%
a 81
 
4.5%
Other values (9) 262
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1140
62.9%
Space Separator 405
 
22.4%
Decimal Number 265
 
14.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 169
14.8%
o 163
14.3%
p 136
11.9%
n 130
11.4%
d 129
11.3%
w 129
11.3%
a 81
7.1%
t 67
 
5.9%
c 35
 
3.1%
i 34
 
3.0%
Other values (4) 67
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 125
47.2%
5 82
30.9%
4 58
21.9%
Space Separator
ValueCountFrequency (%)
405
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
62.9%
Common 671
37.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 169
14.8%
o 163
14.3%
p 136
11.9%
n 130
11.4%
d 129
11.3%
w 129
11.3%
a 81
7.1%
t 67
 
5.9%
c 35
 
3.1%
i 34
 
3.0%
Other values (4) 67
 
5.9%
Common
ValueCountFrequency (%)
405
60.4%
1 125
 
18.6%
5 82
 
12.2%
4 58
 
8.6%
/ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
405
22.4%
u 169
9.3%
o 163
9.0%
p 136
 
7.5%
n 130
 
7.2%
d 129
 
7.1%
w 129
 
7.1%
1 125
 
6.9%
5 82
 
4.5%
a 81
 
4.5%
Other values (9) 262
14.5%

clutch
Categorical

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
wet multiplate
90 
automatic
32 
assist and slipper clutch
22 
wet multiplate with assist and slipper clutch
21 
wet multiplate with torque assist clutch
 
8

Length

Max length45
Median length14
Mean length19.439306
Min length9

Characters and Unicode

Total characters3363
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwet multiplate
2nd rowwet multiplate
3rd rowwet multiplate
4th rowassist and slipper clutch
5th rowwet multiplate

Common Values

ValueCountFrequency (%)
wet multiplate 90
52.0%
automatic 32
 
18.5%
assist and slipper clutch 22
 
12.7%
wet multiplate with assist and slipper clutch 21
 
12.1%
wet multiplate with torque assist clutch 8
 
4.6%

Length

2023-04-07T17:31:02.138109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:02.260828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wet 119
24.0%
multiplate 119
24.0%
assist 51
10.3%
clutch 51
10.3%
and 43
 
8.7%
slipper 43
 
8.7%
automatic 32
 
6.5%
with 29
 
5.9%
torque 8
 
1.6%

Most occurring characters

ValueCountFrequency (%)
t 560
16.7%
l 332
9.9%
322
9.6%
e 289
8.6%
a 277
8.2%
i 274
8.1%
u 210
 
6.2%
p 205
 
6.1%
s 196
 
5.8%
m 151
 
4.5%
Other values (8) 547
16.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3041
90.4%
Space Separator 322
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 560
18.4%
l 332
10.9%
e 289
9.5%
a 277
9.1%
i 274
9.0%
u 210
 
6.9%
p 205
 
6.7%
s 196
 
6.4%
m 151
 
5.0%
w 148
 
4.9%
Other values (7) 399
13.1%
Space Separator
ValueCountFrequency (%)
322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3041
90.4%
Common 322
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 560
18.4%
l 332
10.9%
e 289
9.5%
a 277
9.1%
i 274
9.0%
u 210
 
6.9%
p 205
 
6.7%
s 196
 
6.4%
m 151
 
5.0%
w 148
 
4.9%
Other values (7) 399
13.1%
Common
ValueCountFrequency (%)
322
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 560
16.7%
l 332
9.9%
322
9.6%
e 289
8.6%
a 277
8.2%
i 274
8.1%
u 210
 
6.2%
p 205
 
6.1%
s 196
 
5.8%
m 151
 
4.5%
Other values (8) 547
16.3%

fuel tank capacity
Real number (ℝ)

Distinct49
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.446243
Minimum4
Maximum24.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:02.384941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q110
median12.8
Q315
95-th percentile20
Maximum24.5
Range20.5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3276222
Coefficient of variation (CV)0.34770511
Kurtosis-0.24634658
Mean12.446243
Median Absolute Deviation (MAD)2.3
Skewness0.0090763457
Sum2153.2
Variance18.728314
MonotonicityNot monotonic
2023-04-07T17:31:02.501374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12 23
 
13.3%
13 12
 
6.9%
15 10
 
5.8%
10 10
 
5.8%
14 10
 
5.8%
17 9
 
5.2%
11 7
 
4.0%
14.5 6
 
3.5%
20 6
 
3.5%
7.4 6
 
3.5%
Other values (39) 74
42.8%
ValueCountFrequency (%)
4 2
 
1.2%
4.2 1
 
0.6%
4.8 2
 
1.2%
5 5
2.9%
5.1 1
 
0.6%
5.2 2
 
1.2%
5.3 4
2.3%
5.5 2
 
1.2%
5.8 1
 
0.6%
6 4
2.3%
ValueCountFrequency (%)
24.5 1
 
0.6%
22 1
 
0.6%
21.1 1
 
0.6%
21 3
 
1.7%
20 6
3.5%
19 3
 
1.7%
18.5 1
 
0.6%
18 4
2.3%
17.9 1
 
0.6%
17 9
5.2%

mileage - arai
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)55.3%
Missing135
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean21.910526
Minimum13
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:02.608626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14.3825
Q117.85
median19.3
Q324
95-th percentile39
Maximum42
Range29
Interquartile range (IQR)6.15

Descriptive statistics

Standard deviation7.0434382
Coefficient of variation (CV)0.32146367
Kurtosis1.9492814
Mean21.910526
Median Absolute Deviation (MAD)3.2
Skewness1.5081201
Sum832.6
Variance49.610021
MonotonicityNot monotonic
2023-04-07T17:31:02.704072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
19 6
 
3.5%
24 3
 
1.7%
25 3
 
1.7%
15 2
 
1.2%
39 2
 
1.2%
23 2
 
1.2%
21.7 2
 
1.2%
17.8 2
 
1.2%
18 2
 
1.2%
17 2
 
1.2%
Other values (11) 12
 
6.9%
(Missing) 135
78.0%
ValueCountFrequency (%)
13 1
 
0.6%
13.15 1
 
0.6%
14.6 1
 
0.6%
15 2
 
1.2%
16.6 1
 
0.6%
17 2
 
1.2%
17.8 2
 
1.2%
18 2
 
1.2%
18.5 1
 
0.6%
19 6
3.5%
ValueCountFrequency (%)
42 1
 
0.6%
39 2
1.2%
36 1
 
0.6%
31 1
 
0.6%
25 3
1.7%
24 3
1.7%
23.55 1
 
0.6%
23 2
1.2%
21.7 2
1.2%
21 1
 
0.6%

mileage - owner reported
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)39.1%
Missing63
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean43.927273
Minimum15
Maximum73.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:02.811615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile29
Q135
median45
Q350
95-th percentile65
Maximum73.5
Range58.5
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.200392
Coefficient of variation (CV)0.25497582
Kurtosis0.21261619
Mean43.927273
Median Absolute Deviation (MAD)6.75
Skewness0.4263784
Sum4832
Variance125.44879
MonotonicityNot monotonic
2023-04-07T17:31:02.923984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
45 15
 
8.7%
35 10
 
5.8%
40 10
 
5.8%
50 9
 
5.2%
30 6
 
3.5%
55 5
 
2.9%
65 4
 
2.3%
48 3
 
1.7%
46 3
 
1.7%
70 3
 
1.7%
Other values (33) 42
24.3%
(Missing) 63
36.4%
ValueCountFrequency (%)
15 1
 
0.6%
23 1
 
0.6%
25 1
 
0.6%
26 1
 
0.6%
28 1
 
0.6%
29 2
 
1.2%
30 6
3.5%
30.5 1
 
0.6%
31 1
 
0.6%
32 2
 
1.2%
ValueCountFrequency (%)
73.5 1
 
0.6%
70 3
1.7%
68 1
 
0.6%
65 4
2.3%
60 2
 
1.2%
59 1
 
0.6%
58 1
 
0.6%
57 1
 
0.6%
55 5
2.9%
52 1
 
0.6%

top speed
Real number (ℝ)

Distinct68
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.13757
Minimum58
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:03.038370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile84.2
Q195
median130
Q3180
95-th percentile299
Maximum400
Range342
Interquartile range (IQR)85

Descriptive statistics

Standard deviation67.856225
Coefficient of variation (CV)0.45196032
Kurtosis0.82402676
Mean150.13757
Median Absolute Deviation (MAD)40
Skewness1.2012997
Sum25973.8
Variance4604.4672
MonotonicityNot monotonic
2023-04-07T17:31:03.288482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 21
 
12.1%
299 12
 
6.9%
115 10
 
5.8%
95 9
 
5.2%
150 6
 
3.5%
130 6
 
3.5%
120 5
 
2.9%
85 4
 
2.3%
180 4
 
2.3%
200 4
 
2.3%
Other values (58) 92
53.2%
ValueCountFrequency (%)
58 2
 
1.2%
66 1
 
0.6%
75 2
 
1.2%
78 1
 
0.6%
80 2
 
1.2%
83 1
 
0.6%
85 4
 
2.3%
86 1
 
0.6%
87 2
 
1.2%
90 21
12.1%
ValueCountFrequency (%)
400 1
 
0.6%
316 1
 
0.6%
305 1
 
0.6%
299 12
6.9%
297 1
 
0.6%
280 1
 
0.6%
270 1
 
0.6%
265 1
 
0.6%
244 1
 
0.6%
240 2
 
1.2%

braking system
Categorical

Distinct7
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
dual channel abs
79 
single channel abs
39 
cbs
22 
ibs
13 
sbt
11 
Other values (2)

Length

Max length18
Median length16
Mean length12.763006
Min length3

Characters and Unicode

Total characters2208
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle channel abs
2nd rowsingle channel abs
3rd rowsingle channel abs
4th rowdual channel abs
5th rowdual channel abs

Common Values

ValueCountFrequency (%)
dual channel abs 79
45.7%
single channel abs 39
22.5%
cbs 22
 
12.7%
ibs 13
 
7.5%
sbt 11
 
6.4%
switchable abs 7
 
4.0%
ubs 2
 
1.2%

Length

2023-04-07T17:31:03.407773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:03.547079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
abs 125
30.0%
channel 118
28.4%
dual 79
19.0%
single 39
 
9.4%
cbs 22
 
5.3%
ibs 13
 
3.1%
sbt 11
 
2.6%
switchable 7
 
1.7%
ubs 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 329
14.9%
n 275
12.5%
l 243
11.0%
243
11.0%
s 219
9.9%
b 180
8.2%
e 164
7.4%
c 147
6.7%
h 125
 
5.7%
u 81
 
3.7%
Other values (5) 202
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1965
89.0%
Space Separator 243
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 329
16.7%
n 275
14.0%
l 243
12.4%
s 219
11.1%
b 180
9.2%
e 164
8.3%
c 147
7.5%
h 125
 
6.4%
u 81
 
4.1%
d 79
 
4.0%
Other values (4) 123
 
6.3%
Space Separator
ValueCountFrequency (%)
243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1965
89.0%
Common 243
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 329
16.7%
n 275
14.0%
l 243
12.4%
s 219
11.1%
b 180
9.2%
e 164
8.3%
c 147
7.5%
h 125
 
6.4%
u 81
 
4.1%
d 79
 
4.0%
Other values (4) 123
 
6.3%
Common
ValueCountFrequency (%)
243
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 329
14.9%
n 275
12.5%
l 243
11.0%
243
11.0%
s 219
9.9%
b 180
8.2%
e 164
7.4%
c 147
6.7%
h 125
 
5.7%
u 81
 
3.7%
Other values (5) 202
9.1%

front brake type
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
disc
135 
drum
38 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters692
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdisc
2nd rowdisc
3rd rowdisc
4th rowdisc
5th rowdisc

Common Values

ValueCountFrequency (%)
disc 135
78.0%
drum 38
 
22.0%

Length

2023-04-07T17:31:03.663076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:03.776711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
disc 135
78.0%
drum 38
 
22.0%

Most occurring characters

ValueCountFrequency (%)
d 173
25.0%
i 135
19.5%
s 135
19.5%
c 135
19.5%
r 38
 
5.5%
u 38
 
5.5%
m 38
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 692
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 173
25.0%
i 135
19.5%
s 135
19.5%
c 135
19.5%
r 38
 
5.5%
u 38
 
5.5%
m 38
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 692
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 173
25.0%
i 135
19.5%
s 135
19.5%
c 135
19.5%
r 38
 
5.5%
u 38
 
5.5%
m 38
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 173
25.0%
i 135
19.5%
s 135
19.5%
c 135
19.5%
r 38
 
5.5%
u 38
 
5.5%
m 38
 
5.5%

front brake size
Real number (ℝ)

Distinct23
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.81503
Minimum110
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:03.873178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile130
Q1200
median286
Q3310
95-th percentile330
Maximum330
Range220
Interquartile range (IQR)110

Descriptive statistics

Standard deviation74.312746
Coefficient of variation (CV)0.29049406
Kurtosis-0.80667396
Mean255.81503
Median Absolute Deviation (MAD)34
Skewness-0.90483182
Sum44256
Variance5522.3842
MonotonicityNot monotonic
2023-04-07T17:31:03.982288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
130 30
17.3%
320 30
17.3%
300 28
16.2%
310 15
8.7%
330 11
 
6.4%
280 8
 
4.6%
276 8
 
4.6%
282 7
 
4.0%
200 6
 
3.5%
110 4
 
2.3%
Other values (13) 26
15.0%
ValueCountFrequency (%)
110 4
 
2.3%
120 2
 
1.2%
130 30
17.3%
150 1
 
0.6%
170 1
 
0.6%
190 2
 
1.2%
200 6
 
3.5%
220 4
 
2.3%
230 1
 
0.6%
240 4
 
2.3%
ValueCountFrequency (%)
330 11
 
6.4%
320 30
17.3%
310 15
8.7%
300 28
16.2%
296 1
 
0.6%
290 1
 
0.6%
286 1
 
0.6%
282 7
 
4.0%
280 8
 
4.6%
276 8
 
4.6%

rear tyre size
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct61
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
90/100 - 10
 
12
160/60 - zr17
 
10
130/70 - 17
 
9
80/100 - 18
 
8
150/70 - r17
 
8
Other values (56)
126 

Length

Max length19
Median length15
Mean length11.757225
Min length9

Characters and Unicode

Total characters2034
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)11.6%

Sample

1st row120/80 - 17
2nd row120/80 - 18
3rd row3.25 x 19 - 60p
4th row130/70 - 18
5th row140/70 - 17

Common Values

ValueCountFrequency (%)
90/100 - 10 12
 
6.9%
160/60 - zr17 10
 
5.8%
130/70 - 17 9
 
5.2%
80/100 - 18 8
 
4.6%
150/70 - r17 8
 
4.6%
150/60 - r17 8
 
4.6%
180/55 - zr17 6
 
3.5%
140/60 - r17 6
 
3.5%
120/70 - 10 5
 
2.9%
100/90 - 17 5
 
2.9%
Other values (51) 96
55.5%

Length

2023-04-07T17:31:04.083021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
170
32.0%
17 44
 
8.3%
r17 37
 
7.0%
zr17 31
 
5.8%
18 21
 
3.9%
10 21
 
3.9%
130/70 15
 
2.8%
150/60 13
 
2.4%
180/55 12
 
2.3%
90/100 12
 
2.3%
Other values (46) 156
29.3%

Most occurring characters

ValueCountFrequency (%)
0 378
18.6%
359
17.6%
1 332
16.3%
- 170
8.4%
/ 165
8.1%
7 159
7.8%
5 79
 
3.9%
r 78
 
3.8%
8 66
 
3.2%
6 60
 
2.9%
Other values (10) 188
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1204
59.2%
Space Separator 359
 
17.6%
Other Punctuation 176
 
8.7%
Dash Punctuation 170
 
8.4%
Lowercase Letter 125
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 378
31.4%
1 332
27.6%
7 159
13.2%
5 79
 
6.6%
8 66
 
5.5%
6 60
 
5.0%
9 39
 
3.2%
2 36
 
3.0%
3 28
 
2.3%
4 27
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
r 78
62.4%
z 33
26.4%
x 6
 
4.8%
p 6
 
4.8%
l 2
 
1.6%
Other Punctuation
ValueCountFrequency (%)
/ 165
93.8%
. 9
 
5.1%
" 2
 
1.1%
Space Separator
ValueCountFrequency (%)
359
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1909
93.9%
Latin 125
 
6.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 378
19.8%
359
18.8%
1 332
17.4%
- 170
8.9%
/ 165
8.6%
7 159
8.3%
5 79
 
4.1%
8 66
 
3.5%
6 60
 
3.1%
9 39
 
2.0%
Other values (5) 102
 
5.3%
Latin
ValueCountFrequency (%)
r 78
62.4%
z 33
26.4%
x 6
 
4.8%
p 6
 
4.8%
l 2
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 378
18.6%
359
17.6%
1 332
16.3%
- 170
8.4%
/ 165
8.1%
7 159
7.8%
5 79
 
3.9%
r 78
 
3.8%
8 66
 
3.2%
6 60
 
2.9%
Other values (10) 188
9.2%

tyre type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
tubeless
155 
tubed
18 

Length

Max length8
Median length8
Mean length7.6878613
Min length5

Characters and Unicode

Total characters1330
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtubed
2nd rowtubed
3rd rowtubed
4th rowtubed
5th rowtubeless

Common Values

ValueCountFrequency (%)
tubeless 155
89.6%
tubed 18
 
10.4%

Length

2023-04-07T17:31:04.185097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:04.287925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tubeless 155
89.6%
tubed 18
 
10.4%

Most occurring characters

ValueCountFrequency (%)
e 328
24.7%
s 310
23.3%
t 173
13.0%
u 173
13.0%
b 173
13.0%
l 155
11.7%
d 18
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1330
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 328
24.7%
s 310
23.3%
t 173
13.0%
u 173
13.0%
b 173
13.0%
l 155
11.7%
d 18
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1330
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 328
24.7%
s 310
23.3%
t 173
13.0%
u 173
13.0%
b 173
13.0%
l 155
11.7%
d 18
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 328
24.7%
s 310
23.3%
t 173
13.0%
u 173
13.0%
b 173
13.0%
l 155
11.7%
d 18
 
1.4%
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size301.0 B
True
94 
False
79 
ValueCountFrequency (%)
True 94
54.3%
False 79
45.7%
2023-04-07T17:31:04.388902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

rear brake type
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
disc
107 
drum
66 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters692
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdrum
2nd rowdisc
3rd rowdrum
4th rowdisc
5th rowdisc

Common Values

ValueCountFrequency (%)
disc 107
61.8%
drum 66
38.2%

Length

2023-04-07T17:31:04.481753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:04.584690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
disc 107
61.8%
drum 66
38.2%

Most occurring characters

ValueCountFrequency (%)
d 173
25.0%
i 107
15.5%
s 107
15.5%
c 107
15.5%
r 66
 
9.5%
u 66
 
9.5%
m 66
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 692
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 173
25.0%
i 107
15.5%
s 107
15.5%
c 107
15.5%
r 66
 
9.5%
u 66
 
9.5%
m 66
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 692
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 173
25.0%
i 107
15.5%
s 107
15.5%
c 107
15.5%
r 66
 
9.5%
u 66
 
9.5%
m 66
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 173
25.0%
i 107
15.5%
s 107
15.5%
c 107
15.5%
r 66
 
9.5%
u 66
 
9.5%
m 66
 
9.5%

rear brake size
Real number (ℝ)

Distinct20
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.43931
Minimum110
Maximum316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:04.665913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile116
Q1130
median220
Q3240
95-th percentile262
Maximum316
Range206
Interquartile range (IQR)110

Descriptive statistics

Standard deviation55.517785
Coefficient of variation (CV)0.28118912
Kurtosis-1.5098487
Mean197.43931
Median Absolute Deviation (MAD)35
Skewness-0.30904865
Sum34157
Variance3082.2245
MonotonicityNot monotonic
2023-04-07T17:31:04.749935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
130 39
22.5%
220 26
15.0%
240 18
10.4%
230 16
9.2%
245 13
 
7.5%
140 11
 
6.4%
255 9
 
5.2%
110 9
 
5.2%
250 6
 
3.5%
260 5
 
2.9%
Other values (10) 21
12.1%
ValueCountFrequency (%)
110 9
 
5.2%
120 3
 
1.7%
130 39
22.5%
140 11
 
6.4%
153 4
 
2.3%
193 1
 
0.6%
200 1
 
0.6%
220 26
15.0%
225 2
 
1.2%
230 16
9.2%
ValueCountFrequency (%)
316 1
 
0.6%
300 1
 
0.6%
270 3
 
1.7%
265 4
 
2.3%
260 5
 
2.9%
256 1
 
0.6%
255 9
5.2%
250 6
 
3.5%
245 13
7.5%
240 18
10.4%

wheel type
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
alloy
140 
spoke
26 
steel
 
7

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters865
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowspoke
2nd rowspoke
3rd rowspoke
4th rowspoke
5th rowalloy

Common Values

ValueCountFrequency (%)
alloy 140
80.9%
spoke 26
 
15.0%
steel 7
 
4.0%

Length

2023-04-07T17:31:04.846859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-07T17:31:04.958688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
alloy 140
80.9%
spoke 26
 
15.0%
steel 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
l 287
33.2%
o 166
19.2%
a 140
16.2%
y 140
16.2%
e 40
 
4.6%
s 33
 
3.8%
p 26
 
3.0%
k 26
 
3.0%
t 7
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 865
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 287
33.2%
o 166
19.2%
a 140
16.2%
y 140
16.2%
e 40
 
4.6%
s 33
 
3.8%
p 26
 
3.0%
k 26
 
3.0%
t 7
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 865
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 287
33.2%
o 166
19.2%
a 140
16.2%
y 140
16.2%
e 40
 
4.6%
s 33
 
3.8%
p 26
 
3.0%
k 26
 
3.0%
t 7
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 287
33.2%
o 166
19.2%
a 140
16.2%
y 140
16.2%
e 40
 
4.6%
s 33
 
3.8%
p 26
 
3.0%
k 26
 
3.0%
t 7
 
0.8%

front wheel size
Real number (ℝ)

Distinct9
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.468208
Minimum10
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:05.048839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q117
median17
Q318
95-th percentile19
Maximum21
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4100988
Coefficient of variation (CV)0.14634858
Kurtosis1.1547801
Mean16.468208
Median Absolute Deviation (MAD)0
Skewness-1.3647213
Sum2849
Variance5.8085764
MonotonicityNot monotonic
2023-04-07T17:31:05.150697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
17 92
53.2%
18 27
 
15.6%
19 17
 
9.8%
12 16
 
9.2%
10 6
 
3.5%
11 5
 
2.9%
16 4
 
2.3%
21 3
 
1.7%
14 3
 
1.7%
ValueCountFrequency (%)
10 6
 
3.5%
11 5
 
2.9%
12 16
 
9.2%
14 3
 
1.7%
16 4
 
2.3%
17 92
53.2%
18 27
 
15.6%
19 17
 
9.8%
21 3
 
1.7%
ValueCountFrequency (%)
21 3
 
1.7%
19 17
 
9.8%
18 27
 
15.6%
17 92
53.2%
16 4
 
2.3%
14 3
 
1.7%
12 16
 
9.2%
11 5
 
2.9%
10 6
 
3.5%

rear wheel size
Real number (ℝ)

Distinct8
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.00578
Minimum10
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:05.236892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q117
median17
Q317
95-th percentile18
Maximum19
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4999933
Coefficient of variation (CV)0.15619315
Kurtosis1.4865294
Mean16.00578
Median Absolute Deviation (MAD)0
Skewness-1.7503484
Sum2769
Variance6.2499664
MonotonicityNot monotonic
2023-04-07T17:31:05.314018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
17 112
64.7%
18 22
 
12.7%
10 21
 
12.1%
12 6
 
3.5%
16 6
 
3.5%
14 3
 
1.7%
15 2
 
1.2%
19 1
 
0.6%
ValueCountFrequency (%)
10 21
 
12.1%
12 6
 
3.5%
14 3
 
1.7%
15 2
 
1.2%
16 6
 
3.5%
17 112
64.7%
18 22
 
12.7%
19 1
 
0.6%
ValueCountFrequency (%)
19 1
 
0.6%
18 22
 
12.7%
17 112
64.7%
16 6
 
3.5%
15 2
 
1.2%
14 3
 
1.7%
12 6
 
3.5%
10 21
 
12.1%

front tyre size
Categorical

Distinct43
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
120/70 - zr17
27 
100/80 - 17
19 
90/90 - 12
12 
80/100 - 18
12 
100/90 - 19
11 
Other values (38)
92 

Length

Max length19
Median length15
Mean length11.531792
Min length9

Characters and Unicode

Total characters1995
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)11.0%

Sample

1st row100/80 - 17
2nd row100/90 - 19
3rd row3.25 x 19 - 54p
4th row100/90 - 18
5th row100/90 - 19

Common Values

ValueCountFrequency (%)
120/70 - zr17 27
15.6%
100/80 - 17 19
 
11.0%
90/90 - 12 12
 
6.9%
80/100 - 18 12
 
6.9%
100/90 - 19 11
 
6.4%
110/70 - r17 10
 
5.8%
80/100 - 17 8
 
4.6%
90/90 - 17 7
 
4.0%
110/70 - 17 7
 
4.0%
100/90 - 18 6
 
3.5%
Other values (33) 54
31.2%

Length

2023-04-07T17:31:05.410345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
170
31.9%
17 49
 
9.2%
120/70 43
 
8.1%
zr17 28
 
5.3%
90/90 23
 
4.3%
110/70 23
 
4.3%
18 22
 
4.1%
100/80 20
 
3.8%
80/100 20
 
3.8%
100/90 17
 
3.2%
Other values (36) 118
22.1%

Most occurring characters

ValueCountFrequency (%)
0 398
19.9%
360
18.0%
1 351
17.6%
- 170
8.5%
7 165
8.3%
/ 163
8.2%
9 89
 
4.5%
8 74
 
3.7%
2 73
 
3.7%
r 58
 
2.9%
Other values (13) 94
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1180
59.1%
Space Separator 360
 
18.0%
Other Punctuation 174
 
8.7%
Dash Punctuation 170
 
8.5%
Lowercase Letter 111
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 398
33.7%
1 351
29.7%
7 165
14.0%
9 89
 
7.5%
8 74
 
6.3%
2 73
 
6.2%
4 12
 
1.0%
5 10
 
0.8%
6 5
 
0.4%
3 3
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
r 58
52.3%
z 33
29.7%
x 6
 
5.4%
p 6
 
5.4%
l 2
 
1.8%
m 2
 
1.8%
t 2
 
1.8%
b 2
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 163
93.7%
. 9
 
5.2%
" 2
 
1.1%
Space Separator
ValueCountFrequency (%)
360
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1884
94.4%
Latin 111
 
5.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 398
21.1%
360
19.1%
1 351
18.6%
- 170
9.0%
7 165
8.8%
/ 163
8.7%
9 89
 
4.7%
8 74
 
3.9%
2 73
 
3.9%
4 12
 
0.6%
Other values (5) 29
 
1.5%
Latin
ValueCountFrequency (%)
r 58
52.3%
z 33
29.7%
x 6
 
5.4%
p 6
 
5.4%
l 2
 
1.8%
m 2
 
1.8%
t 2
 
1.8%
b 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 398
19.9%
360
18.0%
1 351
17.6%
- 170
8.5%
7 165
8.3%
/ 163
8.2%
9 89
 
4.5%
8 74
 
3.7%
2 73
 
3.7%
r 58
 
2.9%
Other values (13) 94
 
4.7%
Distinct18
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.215896
Minimum20
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:05.535770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q125
median28
Q332
95-th percentile36
Maximum42
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1919878
Coefficient of variation (CV)0.18400932
Kurtosis-0.76144002
Mean28.215896
Median Absolute Deviation (MAD)4
Skewness0.28722191
Sum4881.35
Variance26.956737
MonotonicityNot monotonic
2023-04-07T17:31:05.627238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
25 46
26.6%
29 23
13.3%
32 22
12.7%
36 21
12.1%
20 13
 
7.5%
22 13
 
7.5%
33 10
 
5.8%
24 5
 
2.9%
28 5
 
2.9%
34 3
 
1.7%
Other values (8) 12
 
6.9%
ValueCountFrequency (%)
20 13
 
7.5%
21 2
 
1.2%
22 13
 
7.5%
23 2
 
1.2%
24 5
 
2.9%
25 46
26.6%
26 1
 
0.6%
28 5
 
2.9%
29 23
13.3%
30 2
 
1.2%
ValueCountFrequency (%)
42 2
 
1.2%
40 1
 
0.6%
36 21
12.1%
35 1
 
0.6%
34 3
 
1.7%
33.35 1
 
0.6%
33 10
5.8%
32 22
12.7%
30 2
 
1.2%
29 23
13.3%
Distinct16
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.12289
Minimum25
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:05.718554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile26
Q128
median32
Q336
95-th percentile42
Maximum42
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7200925
Coefficient of variation (CV)0.1469386
Kurtosis-0.46357827
Mean32.12289
Median Absolute Deviation (MAD)4
Skewness0.66518497
Sum5557.26
Variance22.279273
MonotonicityNot monotonic
2023-04-07T17:31:05.811378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
36 34
19.7%
28 33
19.1%
29 29
16.8%
42 17
9.8%
33 16
9.2%
32 13
 
7.5%
26 11
 
6.4%
30 5
 
2.9%
25 4
 
2.3%
34 3
 
1.7%
Other values (6) 8
 
4.6%
ValueCountFrequency (%)
25 4
 
2.3%
26 11
 
6.4%
28 33
19.1%
28.5 2
 
1.2%
29 29
16.8%
30 5
 
2.9%
31 1
 
0.6%
32 13
 
7.5%
33 16
9.2%
34 3
 
1.7%
ValueCountFrequency (%)
42 17
9.8%
40 1
 
0.6%
38 1
 
0.6%
36.26 1
 
0.6%
36 34
19.7%
35 2
 
1.2%
34 3
 
1.7%
33 16
9.2%
32 13
 
7.5%
31 1
 
0.6%
Distinct20
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.44659
Minimum20
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:05.913843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q125
median28
Q332
95-th percentile36
Maximum42
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.3541573
Coefficient of variation (CV)0.1882179
Kurtosis-0.88916203
Mean28.44659
Median Absolute Deviation (MAD)4
Skewness0.29887791
Sum4921.26
Variance28.667001
MonotonicityNot monotonic
2023-04-07T17:31:06.001499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
25 44
25.4%
36 24
13.9%
29 22
12.7%
32 19
11.0%
22 15
 
8.7%
20 11
 
6.4%
33 7
 
4.0%
24 5
 
2.9%
28 5
 
2.9%
34 4
 
2.3%
Other values (10) 17
 
9.8%
ValueCountFrequency (%)
20 11
 
6.4%
21 2
 
1.2%
22 15
 
8.7%
23 2
 
1.2%
24 5
 
2.9%
25 44
25.4%
26 1
 
0.6%
27 2
 
1.2%
28 5
 
2.9%
29 22
12.7%
ValueCountFrequency (%)
42 2
 
1.2%
40 1
 
0.6%
38 2
 
1.2%
36.26 1
 
0.6%
36 24
13.9%
35 2
 
1.2%
34 4
 
2.3%
33 7
 
4.0%
32 19
11.0%
30 2
 
1.2%
Distinct15
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.491676
Minimum25
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:06.087593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile30
Q132
median36
Q340
95-th percentile42
Maximum42
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.2260141
Coefficient of variation (CV)0.11907057
Kurtosis-0.93929928
Mean35.491676
Median Absolute Deviation (MAD)4
Skewness0.18205648
Sum6140.06
Variance17.859195
MonotonicityNot monotonic
2023-04-07T17:31:06.179139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
32 48
27.7%
36 39
22.5%
42 26
15.0%
33 16
 
9.2%
41 11
 
6.4%
39 6
 
3.5%
40 6
 
3.5%
30 6
 
3.5%
29 4
 
2.3%
38 3
 
1.7%
Other values (5) 8
 
4.6%
ValueCountFrequency (%)
25 2
 
1.2%
28 2
 
1.2%
29 4
 
2.3%
30 6
 
3.5%
31 1
 
0.6%
32 48
27.7%
33 16
 
9.2%
34 2
 
1.2%
36 39
22.5%
38 3
 
1.7%
ValueCountFrequency (%)
42 26
15.0%
41 11
 
6.4%
40.06 1
 
0.6%
40 6
 
3.5%
39 6
 
3.5%
38 3
 
1.7%
36 39
22.5%
34 2
 
1.2%
33 16
 
9.2%
32 48
27.7%

kerb weight
Real number (ℝ)

Distinct106
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.77457
Minimum86
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:06.287930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile104.6
Q1122
median159.5
Q3200
95-th percentile239
Maximum390
Range304
Interquartile range (IQR)78

Descriptive statistics

Standard deviation47.976751
Coefficient of variation (CV)0.2894096
Kurtosis1.5813502
Mean165.77457
Median Absolute Deviation (MAD)40.5
Skewness0.7971971
Sum28679
Variance2301.7687
MonotonicityNot monotonic
2023-04-07T17:31:06.399937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 8
 
4.6%
112 5
 
2.9%
216 4
 
2.3%
118 4
 
2.3%
159 3
 
1.7%
122 3
 
1.7%
166 3
 
1.7%
115 3
 
1.7%
142 3
 
1.7%
104 3
 
1.7%
Other values (96) 134
77.5%
ValueCountFrequency (%)
86 1
 
0.6%
88 1
 
0.6%
93 1
 
0.6%
98 1
 
0.6%
99 1
 
0.6%
103 1
 
0.6%
104 3
1.7%
105 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
ValueCountFrequency (%)
390 1
0.6%
304 1
0.6%
263 1
0.6%
255 1
0.6%
251 1
0.6%
249 1
0.6%
247 1
0.6%
240 1
0.6%
239 2
1.2%
238 1
0.6%

overall length
Real number (ℝ)

Distinct101
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2050.3121
Minimum1556
Maximum2615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:06.524766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1556
5-th percentile1770
Q11990
median2055
Q32122
95-th percentile2269.4
Maximum2615
Range1059
Interquartile range (IQR)132

Descriptive statistics

Standard deviation141.59813
Coefficient of variation (CV)0.069061742
Kurtosis1.9626381
Mean2050.3121
Median Absolute Deviation (MAD)65
Skewness-0.030202644
Sum354704
Variance20050.03
MonotonicityNot monotonic
2023-04-07T17:31:06.649040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1770 7
 
4.0%
1990 6
 
3.5%
2100 6
 
3.5%
2020 5
 
2.9%
2070 5
 
2.9%
2090 5
 
2.9%
2055 4
 
2.3%
2085 4
 
2.3%
2017 3
 
1.7%
1985 3
 
1.7%
Other values (91) 125
72.3%
ValueCountFrequency (%)
1556 1
 
0.6%
1735 1
 
0.6%
1769 2
 
1.2%
1770 7
4.0%
1808 1
 
0.6%
1809 1
 
0.6%
1829 1
 
0.6%
1833 1
 
0.6%
1834 1
 
0.6%
1843 2
 
1.2%
ValueCountFrequency (%)
2615 1
 
0.6%
2500 1
 
0.6%
2310 2
1.2%
2307 1
 
0.6%
2301 1
 
0.6%
2275 1
 
0.6%
2273 1
 
0.6%
2270 1
 
0.6%
2269 1
 
0.6%
2240 3
1.7%

overall width
Real number (ℝ)

Distinct91
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean788.65029
Minimum590
Maximum1020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:06.779017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum590
5-th percentile690
Q1730
median786
Q3831
95-th percentile927
Maximum1020
Range430
Interquartile range (IQR)101

Descriptive statistics

Standard deviation75.28821
Coefficient of variation (CV)0.095464632
Kurtosis0.24189998
Mean788.65029
Median Absolute Deviation (MAD)49
Skewness0.52012982
Sum136436.5
Variance5668.3145
MonotonicityNot monotonic
2023-04-07T17:31:06.904903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
690 7
 
4.0%
800 6
 
3.5%
806 6
 
3.5%
780 6
 
3.5%
810 6
 
3.5%
715 5
 
2.9%
743 4
 
2.3%
720 4
 
2.3%
785 4
 
2.3%
840 4
 
2.3%
Other values (81) 121
69.9%
ValueCountFrequency (%)
590 1
 
0.6%
650 1
 
0.6%
660 1
 
0.6%
670 2
 
1.2%
681 1
 
0.6%
685 2
 
1.2%
690 7
4.0%
697 1
 
0.6%
700 3
1.7%
704 2
 
1.2%
ValueCountFrequency (%)
1020 1
0.6%
1010 1
0.6%
980 1
0.6%
963 1
0.6%
950 1
0.6%
940 1
0.6%
938 1
0.6%
930 2
1.2%
925 1
0.6%
915 2
1.2%

wheelbase
Real number (ℝ)

Distinct88
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1380.3006
Minimum1228
Maximum1695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:07.153967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1228
5-th percentile1238
Q11290
median1365
Q31449
95-th percentile1558.8
Maximum1695
Range467
Interquartile range (IQR)159

Descriptive statistics

Standard deviation97.499266
Coefficient of variation (CV)0.070636256
Kurtosis0.10557496
Mean1380.3006
Median Absolute Deviation (MAD)80
Skewness0.57778119
Sum238792
Variance9506.1068
MonotonicityNot monotonic
2023-04-07T17:31:07.270602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1357 7
 
4.0%
1290 6
 
3.5%
1450 5
 
2.9%
1260 5
 
2.9%
1445 5
 
2.9%
1285 5
 
2.9%
1365 4
 
2.3%
1455 4
 
2.3%
1505 3
 
1.7%
1390 3
 
1.7%
Other values (78) 126
72.8%
ValueCountFrequency (%)
1228 2
 
1.2%
1230 1
 
0.6%
1235 2
 
1.2%
1236 3
1.7%
1238 2
 
1.2%
1245 1
 
0.6%
1250 1
 
0.6%
1255 2
 
1.2%
1260 5
2.9%
1261 2
 
1.2%
ValueCountFrequency (%)
1695 1
0.6%
1677 1
0.6%
1615 1
0.6%
1600 2
1.2%
1594 1
0.6%
1575 1
0.6%
1567 1
0.6%
1560 1
0.6%
1558 1
0.6%
1556 2
1.2%

ground clearance
Real number (ℝ)

Distinct48
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.69075
Minimum115
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:07.402221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile130
Q1150
median162
Q3170
95-th percentile202.4
Maximum238
Range123
Interquartile range (IQR)20

Descriptive statistics

Standard deviation21.825511
Coefficient of variation (CV)0.13498305
Kurtosis0.94324818
Mean161.69075
Median Absolute Deviation (MAD)12
Skewness0.58199636
Sum27972.5
Variance476.35294
MonotonicityNot monotonic
2023-04-07T17:31:07.529101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
165 19
 
11.0%
155 15
 
8.7%
170 13
 
7.5%
180 11
 
6.4%
140 10
 
5.8%
130 10
 
5.8%
160 10
 
5.8%
150 7
 
4.0%
200 5
 
2.9%
145 4
 
2.3%
Other values (38) 69
39.9%
ValueCountFrequency (%)
115 1
 
0.6%
121 2
 
1.2%
122 1
 
0.6%
125 2
 
1.2%
128 2
 
1.2%
130 10
5.8%
131 1
 
0.6%
132 2
 
1.2%
135 4
 
2.3%
140 10
5.8%
ValueCountFrequency (%)
238 1
 
0.6%
220 3
1.7%
218 1
 
0.6%
215 1
 
0.6%
210 1
 
0.6%
203 2
 
1.2%
202 1
 
0.6%
200 5
2.9%
190 1
 
0.6%
187 1
 
0.6%

seat height
Real number (ℝ)

Distinct49
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.42197
Minimum650
Maximum870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:07.687206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum650
5-th percentile741.8
Q1780
median795
Q3810
95-th percentile843.2
Maximum870
Range220
Interquartile range (IQR)30

Descriptive statistics

Standard deviation32.115
Coefficient of variation (CV)0.04042562
Kurtosis3.2518972
Mean794.42197
Median Absolute Deviation (MAD)15
Skewness-1.0173754
Sum137435
Variance1031.3732
MonotonicityNot monotonic
2023-04-07T17:31:07.803660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
790 19
 
11.0%
810 14
 
8.1%
800 13
 
7.5%
795 12
 
6.9%
780 9
 
5.2%
765 8
 
4.6%
770 8
 
4.6%
785 6
 
3.5%
805 6
 
3.5%
830 5
 
2.9%
Other values (39) 73
42.2%
ValueCountFrequency (%)
650 1
0.6%
690 1
0.6%
692 1
0.6%
705 2
1.2%
708 1
0.6%
712 1
0.6%
737 2
1.2%
745 1
0.6%
750 1
0.6%
755 2
1.2%
ValueCountFrequency (%)
870 1
 
0.6%
860 1
 
0.6%
855 2
 
1.2%
850 1
 
0.6%
845 4
2.3%
842 2
 
1.2%
840 5
2.9%
837 1
 
0.6%
835 3
1.7%
830 5
2.9%

overall height
Real number (ℝ)

Distinct87
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1142.3064
Minimum1003
Maximum1523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-04-07T17:31:07.921686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile1044.2
Q11080
median1116
Q31160
95-th percentile1400
Maximum1523
Range520
Interquartile range (IQR)80

Descriptive statistics

Standard deviation99.993743
Coefficient of variation (CV)0.087536712
Kurtosis3.6209926
Mean1142.3064
Median Absolute Deviation (MAD)40
Skewness1.9019615
Sum197619
Variance9998.7486
MonotonicityNot monotonic
2023-04-07T17:31:08.046409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1140 10
 
5.8%
1080 8
 
4.6%
1150 6
 
3.5%
1100 6
 
3.5%
1065 4
 
2.3%
1120 4
 
2.3%
1090 4
 
2.3%
1160 4
 
2.3%
1035 4
 
2.3%
1060 4
 
2.3%
Other values (77) 119
68.8%
ValueCountFrequency (%)
1003 1
 
0.6%
1024 2
1.2%
1028 1
 
0.6%
1035 4
2.3%
1043 1
 
0.6%
1045 1
 
0.6%
1047 1
 
0.6%
1050 4
2.3%
1052 3
1.7%
1055 2
1.2%
ValueCountFrequency (%)
1523 1
 
0.6%
1520 1
 
0.6%
1480 1
 
0.6%
1450 1
 
0.6%
1430 1
 
0.6%
1410 2
1.2%
1405 1
 
0.6%
1400 3
1.7%
1370 1
 
0.6%
1340 1
 
0.6%

chassis type
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct90
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
diamond
14 
diamond type
 
10
tubular steel trellis frame
 
9
trellis, high-tensile steel
 
5
monocoque full - steel body construction
 
4
Other values (85)
131 

Length

Max length64
Median length43
Mean length24.202312
Min length5

Characters and Unicode

Total characters4187
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)31.8%

Sample

1st rowdouble-downtube frame
2nd rowtwin downtube spine frame
3rd rowsingle downtube,using engine as stressed member
4th rowsteel tubular, double cradle frame
5th rowtwin downtube spine frame

Common Values

ValueCountFrequency (%)
diamond 14
 
8.1%
diamond type 10
 
5.8%
tubular steel trellis frame 9
 
5.2%
trellis, high-tensile steel 5
 
2.9%
monocoque full - steel body construction 4
 
2.3%
under bone 4
 
2.3%
high rigidity under bone type 4
 
2.3%
split-trellis frame (tubular) 4
 
2.3%
tubular steel, twin cradle frame 4
 
2.3%
tubular double cradle 4
 
2.3%
Other values (80) 111
64.2%

Length

2023-04-07T17:31:08.177125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
frame 64
 
10.4%
tubular 55
 
9.0%
steel 55
 
9.0%
cradle 35
 
5.7%
diamond 30
 
4.9%
type 24
 
3.9%
trellis 23
 
3.7%
double 18
 
2.9%
with 14
 
2.3%
single 13
 
2.1%
Other values (90) 283
46.1%

Most occurring characters

ValueCountFrequency (%)
e 487
11.6%
441
 
10.5%
l 326
 
7.8%
t 315
 
7.5%
r 267
 
6.4%
a 259
 
6.2%
u 245
 
5.9%
i 233
 
5.6%
s 208
 
5.0%
d 191
 
4.6%
Other values (22) 1215
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3668
87.6%
Space Separator 441
 
10.5%
Other Punctuation 37
 
0.9%
Dash Punctuation 31
 
0.7%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 487
13.3%
l 326
 
8.9%
t 315
 
8.6%
r 267
 
7.3%
a 259
 
7.1%
u 245
 
6.7%
i 233
 
6.4%
s 208
 
5.7%
d 191
 
5.2%
n 186
 
5.1%
Other values (14) 951
25.9%
Other Punctuation
ValueCountFrequency (%)
, 33
89.2%
" 4
 
10.8%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
441
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3668
87.6%
Common 519
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 487
13.3%
l 326
 
8.9%
t 315
 
8.6%
r 267
 
7.3%
a 259
 
7.1%
u 245
 
6.7%
i 233
 
6.4%
s 208
 
5.7%
d 191
 
5.2%
n 186
 
5.1%
Other values (14) 951
25.9%
Common
ValueCountFrequency (%)
441
85.0%
, 33
 
6.4%
- 31
 
6.0%
( 4
 
0.8%
) 4
 
0.8%
" 4
 
0.8%
3 1
 
0.2%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 487
11.6%
441
 
10.5%
l 326
 
7.8%
t 315
 
7.5%
r 267
 
6.4%
a 259
 
6.2%
u 245
 
5.9%
i 233
 
5.6%
s 208
 
5.0%
d 191
 
4.6%
Other values (22) 1215
29.0%

Interactions

2023-04-07T17:30:54.726976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:50.064000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:52.769977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.253915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:57.948002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:00.527668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:03.583118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.208357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:08.941239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:11.603601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:14.691441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.254966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:19.906090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.263094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:24.766929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.215500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:29.646016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:31.989951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:34.527769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:37.067019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:39.745750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.447184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:44.809891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.330893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:49.728146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.308403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:54.826011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:50.185948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:52.874882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.353756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:58.090976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:00.641278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:03.697207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.300297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:09.055115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:11.694965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:14.792674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.371196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:20.006320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.366621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:24.871304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.311113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:29.745191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:32.099400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:34.751469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:37.176193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:39.875248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.545217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:44.912872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.440463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:49.832252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.409176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:54.928866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:50.292118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:52.988055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.455327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:58.203809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:00.750796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:03.801237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.409128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:09.175320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:11.792196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:14.908030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.475863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:20.101792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.453723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:24.967754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.409619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:29.846886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:32.201397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:34.850180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:37.288336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:40.140752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.638903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:45.007416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.544834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:49.937973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.513421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:55.022122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:50.396893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:53.083834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.555803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:58.320372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:00.888612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:03.903342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.505134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:09.297148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:11.894284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:15.032568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.575137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:20.189670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.546085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:25.053366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.495154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:29.941642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:32.309207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:34.940434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:37.404760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:40.250998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.734711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:45.090889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.636446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:50.029887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.601060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:55.109812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:50.486836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:53.174047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.646595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:58.412258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:01.021221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:04.004959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.599027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:09.414148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:12.013146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:15.142144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.659153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:20.271386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.631496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:25.139648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.578907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:30.030918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:32.413734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:35.028686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:37.496063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:40.348147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.816797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:45.170872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.733995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:50.132882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.690207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:55.199716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:50.607070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:53.268391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.746883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:58.505988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:01.136316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:04.106323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.703185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:09.522499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:12.144514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:15.228247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.750599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:20.365306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.716843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:25.233240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.660848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:30.123134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:32.501982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:35.118803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:37.586037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:40.439034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.901147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:45.260913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.830314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:50.218587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-07T17:30:55.294649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:50.713820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:53.362813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.838686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:58.603759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-07T17:30:06.797193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-07T17:30:15.318857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-07T17:30:20.477206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.806872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-07T17:30:27.751550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:30.217424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:32.599686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-07T17:30:37.683463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-07T17:30:56.951780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:52.488842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:54.966695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:57.564128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:00.234811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:03.259811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:05.921454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:08.628772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:11.283156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:14.375442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:16.962141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:19.632882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:21.989939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:24.495949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:26.933954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:29.247337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:31.733703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:34.244703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:36.788689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:39.407053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.167773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:44.539116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.031615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:49.468478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.009823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:54.478531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:57.047171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:52.583103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.067702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:57.667950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:00.349238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:03.367044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.026160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:08.737175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:11.419245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:14.492519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.046220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:19.726157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.090134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:24.591939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.034042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:29.338384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:31.820010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:34.344987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:36.887375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:39.521765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.266017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:44.628818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.132943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:49.554841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.103662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:54.568305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:57.136350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:52.675763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:55.156758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:29:57.787961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:00.432652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:03.473122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:06.115417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:08.851404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:11.507188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:14.596021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:17.141386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:19.809804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:22.175616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:24.677280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:27.121845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:29.560820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:31.906598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:34.431337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:36.971190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:39.627362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:42.349043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:44.714760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:47.250031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:49.640414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:52.215477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-07T17:30:54.643517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-07T17:31:08.340957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0pricemax powermax torquedisplacementborestrokefuel tank capacitymileage - araimileage - owner reportedtop speedfront brake sizerear brake sizefront wheel sizerear wheel sizefront tyre pressure (rider)rear tyre pressure (rider)front tyre pressure (rider & pillion)rear tyre pressure (rider & pillion)kerb weightoverall lengthoverall widthwheelbaseground clearanceseat heightoverall heightbrandcooling systemtransmissiontransmission typecylindersvalves per cylinderspark plugsgear shifting patternclutchbraking systemfront brake typerear tyre sizetyre typeradial tyresrear brake typewheel typefront tyre sizechassis type
Unnamed: 01.0000.6400.5540.5280.5530.5230.2280.361-0.315-0.2480.5580.5280.511-0.050-0.1920.4670.4010.4600.2610.4800.2570.3370.527-0.3720.1620.2410.8270.3500.3440.1100.3990.3520.4840.2880.5090.3970.4960.5140.4000.5130.4610.1640.4320.575
price0.6401.0000.9720.9660.9660.9080.3250.804-0.807-0.8920.9510.9250.8590.3160.1650.8210.7010.8320.4030.9140.6930.6600.897-0.3010.4650.2290.2940.2030.5460.5260.6590.1530.0000.4710.3440.1930.2480.7120.0910.3810.4120.1280.6050.666
max power0.5540.9721.0000.9840.9710.9100.2980.847-0.846-0.8240.9710.9280.8310.3820.2720.8540.7080.8650.4060.9360.7370.6800.893-0.2610.5350.1430.3090.3120.3490.2960.6220.2770.0000.2300.3210.2700.3480.6730.0230.5180.5610.1350.4610.535
max torque0.5280.9660.9841.0000.9900.9220.3840.842-0.707-0.8210.9550.9160.8410.4010.2580.8380.7190.8510.4050.9470.7640.6890.902-0.2610.4730.1610.3680.5000.4560.6350.6770.2730.0000.3180.4180.3090.3700.6310.0800.4550.5710.1620.5530.496
displacement0.5530.9660.9710.9901.0000.9330.3970.825-0.625-0.8610.9370.9040.8450.3570.2000.8030.6980.8180.3850.9430.7520.6790.908-0.2780.4190.1830.4530.5230.5860.7410.7180.2770.0000.4640.4020.3150.4020.6490.2410.4530.5830.1860.6480.613
bore0.5230.9080.9100.9220.9331.0000.3920.751-0.529-0.8540.8950.8750.7970.3610.2010.7530.6170.7720.3040.8730.7130.6550.838-0.2080.4150.1290.3730.4220.4140.4640.4370.3990.1610.3000.5210.4780.6980.5800.1970.6160.7920.2210.4180.376
stroke0.2280.3250.2980.3840.3970.3921.0000.2970.296-0.3640.2870.3630.4630.4090.1940.2150.2130.2380.0880.4210.4910.4610.4490.153-0.0700.1580.4380.2420.5550.5080.6440.3880.0000.4130.4920.2430.3060.5480.3680.2200.2720.3870.5150.575
fuel tank capacity0.3610.8040.8470.8420.8250.7510.2971.000-0.292-0.5140.8240.7830.7480.5280.3990.7410.5770.7640.3160.8670.7490.7010.825-0.0550.5090.1670.3990.3170.6460.7180.3910.6810.0890.4980.5120.4060.6840.5670.1400.5610.7110.3850.5480.529
mileage - arai-0.315-0.807-0.846-0.707-0.625-0.5290.296-0.2921.0000.821-0.689-0.695-0.2230.1570.214-0.620-0.258-0.630-0.488-0.131-0.181-0.274-0.3240.274-0.3880.0750.7410.6880.7490.0000.5870.2340.8980.7490.5070.6391.0000.6440.8980.5110.8980.3350.2150.569
mileage - owner reported-0.248-0.892-0.824-0.821-0.861-0.854-0.364-0.5140.8211.000-0.739-0.805-0.808-0.1200.020-0.267-0.093-0.3010.314-0.764-0.426-0.534-0.7970.171-0.261-0.2270.2930.4110.5670.3450.8110.3660.0000.4860.4130.3640.6550.5310.3300.3630.5630.2770.5640.409
top speed0.5580.9510.9710.9550.9370.8950.2870.824-0.689-0.7391.0000.9140.8080.3560.2630.8620.6980.8720.3980.8970.6940.6410.852-0.2450.5470.1460.3630.3690.5620.4510.5710.3540.1530.4080.5030.4230.7360.5860.0830.6620.8100.2290.5230.507
front brake size0.5280.9250.9280.9160.9040.8750.3630.783-0.695-0.8050.9141.0000.8400.4150.2710.8040.6250.8110.3180.8780.7070.6670.856-0.1800.5600.1790.4270.3410.5200.5230.3180.5500.3320.3860.4670.5170.9500.4950.0370.6920.8730.2360.5110.489
rear brake size0.5110.8590.8310.8410.8450.7970.4630.748-0.223-0.8080.8080.8401.0000.4220.2340.7350.6050.7470.3170.8690.6970.7100.847-0.1200.3880.1980.5570.4580.5590.8550.5100.5640.1310.4110.4620.3840.7680.6520.4450.7180.9820.3960.6540.540
front wheel size-0.0500.3160.3820.4010.3570.3610.4090.5280.157-0.1200.3560.4150.4221.0000.8120.3950.3170.4110.2490.4770.6350.4970.4220.3840.389-0.0140.3810.2470.5050.6710.1330.4840.0000.3800.4970.3490.5180.6960.3450.5750.6440.5390.8880.459
rear wheel size-0.1920.1650.2720.2580.2000.2010.1940.3990.2140.0200.2630.2710.2340.8121.0000.3440.2410.3580.2080.3260.4520.2920.1890.2890.463-0.2580.3670.3160.5630.7790.2250.4550.3080.4260.5130.3270.5180.8240.3420.6380.6720.4060.7700.516
front tyre pressure (rider)0.4670.8210.8540.8380.8030.7530.2150.741-0.620-0.2670.8620.8040.7350.3950.3441.0000.8420.9930.6070.8040.6350.5530.709-0.1950.5540.0380.4640.4110.5450.6320.4850.3980.1180.4240.4870.4120.5030.5630.1850.7090.7630.2620.5280.506
rear tyre pressure (rider)0.4010.7010.7080.7190.6980.6170.2130.577-0.258-0.0930.6980.6250.6050.3170.2410.8421.0000.8430.7560.6770.5250.3850.593-0.2630.3690.1440.4580.2590.3830.3630.5390.4620.1390.2740.3750.3040.3590.4790.1230.4930.5640.2480.4910.427
front tyre pressure (rider & pillion)0.4600.8320.8650.8510.8180.7720.2380.764-0.630-0.3010.8720.8110.7470.4110.3580.9930.8431.0000.6100.8230.6600.5830.731-0.1840.5670.0620.4650.4090.5390.6250.4850.3900.0960.4060.4950.4090.5010.6200.2640.7000.7670.3020.4980.475
rear tyre pressure (rider & pillion)0.2610.4030.4060.4050.3850.3040.0880.316-0.4880.3140.3980.3180.3170.2490.2080.6070.7560.6101.0000.3600.3080.1950.309-0.0990.2460.1870.3350.0940.2560.1160.3290.1150.2960.1740.2370.2380.1100.5770.3790.0650.1940.3210.3710.398
kerb weight0.4800.9140.9360.9470.9430.8730.4210.867-0.131-0.7640.8970.8780.8690.4770.3260.8040.6770.8230.3601.0000.8440.7470.924-0.2030.4340.1670.3670.5880.6790.8570.6920.3950.1460.5190.4870.3840.6980.6790.1220.5890.8010.3110.6750.533
overall length0.2570.6930.7370.7640.7520.7130.4910.749-0.181-0.4260.6940.7070.6970.6350.4520.6350.5250.6600.3080.8441.0000.7280.8280.0210.3560.1400.2490.5040.6750.9190.6510.3890.0000.4960.5020.3340.5310.6540.1770.5060.6590.4510.6930.516
overall width0.3370.6600.6800.6890.6790.6550.4610.701-0.274-0.5340.6410.6670.7100.4970.2920.5530.3850.5830.1950.7470.7281.0000.8000.1210.3790.2310.2880.1780.4230.4890.2690.3810.0880.2960.4330.3560.5530.5810.1840.4420.5530.2770.5690.349
wheelbase0.5270.8970.8930.9020.9080.8380.4490.825-0.324-0.7970.8520.8560.8470.4220.1890.7090.5930.7310.3090.9240.8280.8001.000-0.1480.3560.2650.3620.4770.5370.7920.5590.3930.0000.3980.3670.4390.7840.5660.3330.6430.7860.3500.5190.387
ground clearance-0.372-0.301-0.261-0.261-0.278-0.2080.153-0.0550.2740.171-0.245-0.180-0.1200.3840.289-0.195-0.263-0.184-0.099-0.2030.0210.121-0.1481.0000.1280.0570.2840.2500.3400.3630.3870.3410.0000.2690.3890.2770.2240.5120.1110.4000.4580.1930.4430.372
seat height0.1620.4650.5350.4730.4190.415-0.0700.509-0.388-0.2610.5470.5600.3880.3890.4630.5540.3690.5670.2460.4340.3560.3790.3560.1281.0000.0940.2410.4100.4460.5090.3370.3170.2450.3320.3790.1930.2650.4650.0260.4000.4950.3410.4210.357
overall height0.2410.2290.1430.1610.1830.1290.1580.1670.075-0.2270.1460.1790.198-0.014-0.2580.0380.1440.0620.1870.1670.1400.2310.2650.0570.0941.0000.2240.0900.4160.4550.3700.2670.0000.2930.2970.1250.1330.2230.0000.1420.2250.1610.3200.505
brand0.8270.2940.3090.3680.4530.3730.4380.3990.7410.2930.3630.4270.5570.3810.3670.4640.4580.4650.3350.3670.2490.2880.3620.2840.2410.2241.0000.4130.3920.3610.4850.4600.5380.2950.6640.5770.4900.5140.6280.6140.6180.3640.4750.625
cooling system0.3500.2030.3120.5000.5230.4220.2420.3170.6880.4110.3690.3410.4580.2470.3160.4110.2590.4090.0940.5880.5040.1780.4770.2500.4100.0900.4131.0000.4510.6010.3640.4320.2040.3870.3300.3690.5550.4930.3500.7060.6920.2460.5130.499
transmission0.3440.5460.3490.4560.5860.4140.5550.6460.7490.5670.5620.5200.5590.5050.5630.5450.3830.5390.2560.6790.6750.4230.5370.3400.4460.4160.3920.4511.0000.8570.5960.4470.2470.9910.5360.4960.6800.7480.1350.7020.8020.2930.7640.557
transmission type0.1100.5260.2960.6350.7410.4640.5080.7180.0000.3450.4510.5230.8550.6710.7790.6320.3630.6250.1160.8570.9190.4890.7920.3630.5090.4550.3610.6010.8571.0000.5800.4630.0000.8470.7140.4030.4190.8120.0000.4550.6010.2950.8740.692
cylinders0.3990.6590.6220.6770.7180.4370.6440.3910.5870.8110.5710.3180.5100.1330.2250.4850.5390.4850.3290.6920.6510.2690.5590.3870.3370.3700.4850.3640.5960.5801.0000.2510.0440.5810.3370.3300.3560.6510.0870.5060.5530.2020.6450.685
valves per cylinder0.3520.1530.2770.2730.2770.3990.3880.6810.2340.3660.3540.5500.5640.4840.4550.3980.4620.3900.1150.3950.3890.3810.3930.3410.3170.2670.4600.4320.4470.4630.2511.0000.1090.4280.3370.4250.5290.6930.2850.6510.7090.2360.6620.605
spark plugs0.4840.0000.0000.0000.0000.1610.0000.0890.8980.0000.1530.3320.1310.0000.3080.1180.1390.0960.2960.1460.0000.0880.0000.0000.2450.0000.5380.2040.2470.0000.0440.1091.0000.2290.0000.1580.0000.2750.0000.2110.1070.0000.0000.515
gear shifting pattern0.2880.4710.2300.3180.4640.3000.4130.4980.7490.4860.4080.3860.4110.3800.4260.4240.2740.4060.1740.5190.4960.2960.3980.2690.3320.2930.2950.3870.9910.8470.5810.4280.2291.0000.5200.4340.7220.5950.1240.6980.8080.2680.6010.399
clutch0.5090.3440.3210.4180.4020.5210.4920.5120.5070.4130.5030.4670.4620.4970.5130.4870.3750.4950.2370.4870.5020.4330.3670.3890.3790.2970.6640.3300.5360.7140.3370.3370.0000.5201.0000.3480.4610.5950.0760.5200.6790.3380.5750.660
braking system0.3970.1930.2700.3090.3150.4780.2430.4060.6390.3640.4230.5170.3840.3490.3270.4120.3040.4090.2380.3840.3340.3560.4390.2770.1930.1250.5770.3690.4960.4030.3300.4250.1580.4340.3481.0000.8550.6280.2150.7080.8560.2300.5070.488
front brake type0.4960.2480.3480.3700.4020.6980.3060.6841.0000.6550.7360.9500.7680.5180.5180.5030.3590.5010.1100.6980.5310.5530.7840.2240.2650.1330.4900.5550.6800.4190.3560.5290.0000.7220.4610.8551.0000.7010.0000.5610.6590.3890.7330.514
rear tyre size0.5140.7120.6730.6310.6490.5800.5480.5670.6440.5310.5860.4950.6520.6960.8240.5630.4790.6200.5770.6790.6540.5810.5660.5120.4650.2230.5140.4930.7480.8120.6510.6930.2750.5950.5950.6280.7011.0000.6220.7210.7630.5380.7790.533
tyre type0.4000.0910.0230.0800.2410.1970.3680.1400.8980.3300.0830.0370.4450.3450.3420.1850.1230.2640.3790.1220.1770.1840.3330.1110.0260.0000.6280.3500.1350.0000.0870.2850.0000.1240.0760.2150.0000.6221.0000.3060.1200.5950.5020.475
radial tyres0.5130.3810.5180.4550.4530.6160.2200.5610.5110.3630.6620.6920.7180.5750.6380.7090.4930.7000.0650.5890.5060.4420.6430.4000.4000.1420.6140.7060.7020.4550.5060.6510.2110.6980.5200.7080.5610.7210.3061.0000.7230.2500.6880.519
rear brake type0.4610.4120.5610.5710.5830.7920.2720.7110.8980.5630.8100.8730.9820.6440.6720.7630.5640.7670.1940.8010.6590.5530.7860.4580.4950.2250.6180.6920.8020.6010.5530.7090.1070.8080.6790.8560.6590.7630.1200.7231.0000.2500.8210.580
wheel type0.1640.1280.1350.1620.1860.2210.3870.3850.3350.2770.2290.2360.3960.5390.4060.2620.2480.3020.3210.3110.4510.2770.3500.1930.3410.1610.3640.2460.2930.2950.2020.2360.0000.2680.3380.2300.3890.5380.5950.2500.2501.0000.5530.475
front tyre size0.4320.6050.4610.5530.6480.4180.5150.5480.2150.5640.5230.5110.6540.8880.7700.5280.4910.4980.3710.6750.6930.5690.5190.4430.4210.3200.4750.5130.7640.8740.6450.6620.0000.6010.5750.5070.7330.7790.5020.6880.8210.5531.0000.429
chassis type0.5750.6660.5350.4960.6130.3760.5750.5290.5690.4090.5070.4890.5400.4590.5160.5060.4270.4750.3980.5330.5160.3490.3870.3720.3570.5050.6250.4990.5570.6920.6850.6050.5150.3990.6600.4880.5140.5330.4750.5190.5800.4750.4291.000

Missing values

2023-04-07T17:30:57.341152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-07T17:30:57.903550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-07T17:30:58.183817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0namebrandpricemax powermax torquecooling systemtransmissiontransmission typedisplacementcylindersborestrokevalves per cylinderspark plugsgear shifting patternclutchfuel tank capacitymileage - araimileage - owner reportedtop speedbraking systemfront brake typefront brake sizerear tyre sizetyre typeradial tyresrear brake typerear brake sizewheel typefront wheel sizerear wheel sizefront tyre sizefront tyre pressure (rider)rear tyre pressure (rider)front tyre pressure (rider & pillion)rear tyre pressure (rider & pillion)kerb weightoverall lengthoverall widthwheelbaseground clearanceseat heightoverall heightchassis type
00Royal Enfield Hunter 350Royal Enfield14990020.2027.0air/oil cooled5 speed manualchain drive349.0172.085.821 per cylinder1 down 4 upwet multiplate13.036.036.0114.0single channel absdisc300120/80 - 17tubednodrum153spoke1717100/80 - 1729.032.029.033.0177.02055800.01370150.07901055double-downtube frame
11Royal Enfield Classic 350Royal Enfield19022920.2027.0air/oil cooled5 speed manualchain drive349.0172.085.821 per cylinder1 down 4 upwet multiplate13.0NaN35.0114.0single channel absdisc300120/80 - 18tubednodisc270spoke1918100/90 - 1920.030.022.032.0195.02145785.01390170.08051090twin downtube spine frame
22Royal Enfield Bullet 350Royal Enfield15739119.1028.0air cooled5 speed manualchain drive346.0170.090.012 per cylinder1 down 4 upwet multiplate13.5NaN38.0110.0single channel absdisc2803.25 x 19 - 60ptubednodrum153spoke19193.25 x 19 - 54p20.030.022.032.0186.02170810.01395135.08001120single downtube,using engine as stressed member
33Royal Enfield Continental GT 650Royal Enfield30494547.0052.0air/oil cooled6 speed manualchain drive648.0278.067.821 per cylinder1 down 5 upassist and slipper clutch12.5NaN25.0169.0dual channel absdisc320130/70 - 18tubednodisc240spoke1818100/90 - 1832.036.032.039.0198.02122744.01398174.07931024steel tubular, double cradle frame
44Royal Enfield Meteor 350Royal Enfield20092420.2027.0air/oil cooled5 speed manualchain drive349.0172.085.821 per cylinder1 down 4 upwet multiplate15.0NaN35.0112.0dual channel absdisc300140/70 - 17tubelessyesdisc270alloy1917100/90 - 1932.032.032.036.0191.02140845.01400170.07651140twin downtube spine frame
55Royal Enfield HimalayanRoyal Enfield21586924.3032.0air cooled5 speed manualchain drive411.0178.086.021 per cylinder1 down 4 upwet multiplate15.0NaN30.0120.0switchable absdisc300120/90 - 17tubednodisc240spoke211790/90 - 2125.032.027.034.0199.02190840.01465220.08001370half-duplex split cradle frame
66Royal Enfield Interceptor 650Royal Enfield28814147.0052.0air/oil cooled6 speed manualchain drive648.0278.067.841 per cylinder1 down 5 upassist and slipper clutch13.7NaN23.0169.0dual channel absdisc320130/70 - 18tubednodisc240spoke1818100/90 - 1832.036.032.039.0202.02122789.01400174.08041165steel tubular double cradle
77Royal Enfield Scram 411Royal Enfield20292324.3032.0air cooled5 speed manualchain drive411.0178.086.021 per cylinder1 down 4 upwet multiplate15.0NaN29.0138.0dual channel absdisc300120/90 - 17tubednodisc240spoke1917100/90 - 1925.032.027.034.0185.02160840.01455200.07951165half-duplex split cradle frame
88TVS Raider 125TVS9052411.2011.2air/oil cooled5 speed manualchain drive124.8153.555.531 per cylinder1 down 4 upwet multiplate10.0NaN57.099.0sbtdrum130100/90 - 17tubelessnodrum130alloy171780/100 - 1725.028.025.032.0123.02070785.01326180.07801028single cradle tubular frame
99ApacheTVS11838115.3113.9air cooled5 speed manualchain drive159.7162.052.921 per cylinder1 down 4 upwet multiplate12.0NaN45.0107.0single channel absdisc270110/80 - 17tubelessyesdrum130alloy171790/90 - 1725.028.025.032.0139.02085730.01300180.07901105double cradle synchro stiff
Unnamed: 0namebrandpricemax powermax torquecooling systemtransmissiontransmission typedisplacementcylindersborestrokevalves per cylinderspark plugsgear shifting patternclutchfuel tank capacitymileage - araimileage - owner reportedtop speedbraking systemfront brake typefront brake sizerear tyre sizetyre typeradial tyresrear brake typerear brake sizewheel typefront wheel sizerear wheel sizefront tyre sizefront tyre pressure (rider)rear tyre pressure (rider)front tyre pressure (rider & pillion)rear tyre pressure (rider & pillion)kerb weightoverall lengthoverall widthwheelbaseground clearanceseat heightoverall heightchassis type
163183BonnevilleTriumph113900078.90105.0liquid cooled6 speed manualchain drive1200.00297.680.041 per cylinder1 down 5 upwet multiplate with torque assist clutch14.521.0NaN190.0dual channel absdisc310150/70 - r17tubelessyesdisc255spoke1817100/90 - 1832.036.032.036.0236.02120780.01450140.07901100tubular steel, with twin cradles
164184BonnevilleTriumph128500076.90106.0liquid cooled6 speed manualchain drive1200.00297.680.041 per cylinder1 down 5 upwet multiplate with torque assist clutch12.021.7NaN161.0dual channel absdisc310150/80 - r16tubelessyesdisc255spoke1616mt90 - b1633.036.033.036.0263.02225910.01500140.07051055tubular steel, twin cradle frame
165185Triumph Bonneville T100Triumph100400064.1080.0liquid cooled5 speed manualchain drive900.00284.680.041 per cylinder1 down 4 upwet multiplate with torque assist clutch14.524.0NaN185.0dual channel absdisc310150/70 - r17tubelessyesdisc255spoke1817100/90 - 1832.036.032.036.0228.02230780.01450140.07901100tubular steel, twin cradle frame
166186Triumph Tiger 850 SportTriumph119500084.0082.0liquid cooled6 speed manualchain drive888.00378.061.941 per cylinder1 down 5 upassist and slipper clutch20.019.0NaN160.0dual channel absdisc320150/70 - r17tubelessnodisc255alloy1917100/90 - 1936.042.036.042.0192.02240830.01556203.08101410tubular steel frame, bolt on sub frame
167187Vespa ZX 125Vespa1161359.789.6air cooledautomaticcvt124.45152.058.631 per cylinderautomaticautomatic7.4NaNNaN95.0cbsdisc20090/100 - 10tubelessnodrum140alloy101090/100 - 1020.026.020.032.0114.01770690.01290155.07701140monocoque full-steel body construction
168188Vespa SXL 125Vespa1343139.789.6air cooledautomaticcvt124.45152.058.631 per cylinderautomaticautomatic7.4NaN40.090.0cbsdisc200120/70 - 10tubelessnodrum140alloy1110110/70 - 1120.026.020.032.0114.01770690.01290155.07701140monocoque full steel body construction
169189Vespa SXL 150Vespa14826110.3210.6air cooledautomaticcvt149.50158.056.631 per cylinderautomaticautomatic7.4NaN35.095.0single channel absdisc200120/70 - 10tubelessnodrum140alloy1110110/70 - 1120.026.020.032.0114.01770690.01290155.07701140monocoque full - steel body construction
170190Vespa VXL 125Vespa1304389.789.6air cooledautomaticcvt124.45152.058.631 per cylinderautomaticautomatic7.4NaN39.090.0cbsdisc200120/70 - 10tubelessnodrum140alloy1110110/70 - 1120.026.020.032.0114.01770690.01290155.07701140monocoque full - steel body construction
171191Vespa VXL 150Vespa14389510.3210.6air cooledautomaticcvt149.50158.056.631 per cylinderautomaticautomatic7.4NaN45.095.0single channel absdisc200120/70 - 10tubelessnodrum140alloy1110110/70 - 1120.026.020.032.0114.01770690.01290155.07701140monocoque full - steel body construction
172192Vespa Elegante 150Vespa15592710.3210.6air cooledautomaticcvt149.50160.056.631 per cylinderautomaticautomatic7.4NaN45.0100.0single channel absdisc200120/70 - 10tubelessnodrum140alloy1110110/70 - 1120.026.020.032.0114.01770690.01290155.07701140monocoque full - steel body construction